{"as_of":"2026-08-18T04:21:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8ebcb37957fcd3d32c8a07a472e77c43bcea53c5f04a266f375b7262ddf791cf","coverage":[{"denominator":147,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T19:24:57.553636Z","state":"measured"},{"denominator":100,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":100,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2506.16790/citation-record","integrity":"/paper/2506.16790/integrity","json":"/paper/2506.16790/citation-record.json","paper":"/paper/2506.16790"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.125450Z","title":"Mixhop : Higher-order graph convolutional architectures via sparsified neighborhood mixing","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.125450Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:02c6e0913f72984f178dd389804f4d1f3a484cfd0dcdc4f763940e6a8fec714d","observation_id":"c91fc1ca-4ea5-40ac-9448-e45d6ba2d816","resolution":{"observed_at":"2026-08-15T19:24:57.125450Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.130809Z","title":"On the bottleneck of graph neural networks and its practical implications","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.130809Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:db0785cd95d3b323ef071858eb67c15584582ad2f2399046cdb052b6f1e6c377","observation_id":"a52379f8-dc21-4993-830f-0988e8c5d4f6","resolution":{"observed_at":"2026-08-15T19:24:57.130809Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.135531Z","title":"A machine learning-based approximation of strong branching","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.135531Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:99c617b919c5dc7bdf02888ef861d573a5fd8c9ca13c73b3fa172a01baa9a4fa","observation_id":"fd969f21-f063-4ef4-a6bc-f2c6d66d49a5","resolution":{"observed_at":"2026-08-15T19:24:57.135531Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.140228Z","title":"Label propagation across graphs: Node classification using graph neural tangent kernels","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.140228Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:a374ca70a6542573347bee7f058edc1ec87922d98e3f0aac4f35779975b57bd5","observation_id":"2e828d5d-d28b-4df0-a410-c057ec0dff3a","resolution":{"observed_at":"2026-08-15T19:24:57.140228Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.144838Z","title":"Machine learning for combinatorial optimization: a methodological tour d’horizon","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.144838Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:57c9cb7e91feed2249e0548116b3eec9ec901cad004669d2b005bdba56cdc2e3","observation_id":"f68589fe-2721-4a7f-b982-8e87edd73a6c","resolution":{"observed_at":"2026-08-15T19:24:57.144838Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.148857Z","title":"Decision diagrams for optimization, volume 1","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.148857Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:2f99b3b893b0f0f331f6cddc160f7e5467c20281075571221ccb372a0a8e137b","observation_id":"a04667d6-c75c-4feb-a006-d98c8a2f6655","resolution":{"observed_at":"2026-08-15T19:24:57.148857Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.152982Z","title":"The maximum clique problem","venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.152982Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:951709d3fe05aac5ada6f9080172719eca06683e5dc0f6d585a68d0e0ef7bc73","observation_id":"1ea5bf44-2c9c-4e1c-9040-9c01e1010c1e","resolution":{"observed_at":"2026-08-15T19:24:57.152982Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.157230Z","title":"Can graph neural networks go deeper without over-smoothing? yes, with a randomized path exploration! In IEEE Transactions on Emerging Topics in Computational Intelligence","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.157230Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:d8c991adaa485dfb4a011a0b150dad3c7c97e675a7eba28989163ecd9de4507c","observation_id":"e053edc7-435d-4efa-98a7-77ba6f07dadb","resolution":{"observed_at":"2026-08-15T19:24:57.157230Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.161303Z","title":"Reconnaissance de la parole par reseaux connexionnistes","venue":null,"work_id":null,"year":1988},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.161303Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:7425eb8db94b0456808edd03893986a517fb8ef2713627430edde2f4ff42683a","observation_id":"885cb1ff-2ffa-4961-99ab-793aa64adfc7","resolution":{"observed_at":"2026-08-15T19:24:57.161303Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.165310Z","title":"A survey on optimization metaheuristics","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.165310Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:ffd20ae0002ae0a9c0587eade0e21c7a4b8acccd6739fa24e1bbd90f1966ffa9","observation_id":"722919a5-1b5d-4a2a-9543-99b1138aa357","resolution":{"observed_at":"2026-08-15T19:24:57.165310Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.169064Z","title":"A note on over-smoothing for graph neural networks","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.169064Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:c1cf1b9c992a613bc99c579d09d1790e8fd1291ebcfc22398cb9db23ca7404c5","observation_id":"2dc6f21a-f8a1-4ad6-9540-98f22304748e","resolution":{"observed_at":"2026-08-15T19:24:57.169064Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.173370Z","title":"Measuring and relieving the over-smoothing problem for graph neural networks from the topological view","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.173370Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:5a708f92750870831d3f242315cf1cee487f4791ea49e70f25ef312dfab10e87","observation_id":"836d7a64-8228-41f0-ab1e-5dfc691939dd","resolution":{"observed_at":"2026-08-15T19:24:57.173370Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.178559Z","title":"Dirichlet energy enhancement of graph neural networks by framelet augmentation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.178559Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:28aee1f40a96fb536f01482b57b6ce90c367adbf950cd4ce07f3cb353e68ac2c","observation_id":"33613da8-c84f-4000-81c1-b09d091f8048","resolution":{"observed_at":"2026-08-15T19:24:57.178559Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.15127","last_updated":"2022-05-30T14:19:45Z","snapshot_observed_at":"2026-08-16T16:56:39.005814Z","submitted_at":"2022-05-30T14:19:45Z","title":"Universal Deep GNNs: Rethinking Residual Connection in GNNs from a Path Decomposition Perspective for Preventing the Over-smoothing","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.15127","snapshot_observed_at":"2026-08-15T19:24:57.183603Z","title":"Universal deep gnns: Rethinking residual connection in gnns from a path decomposition perspective for preventing the over-smoothing","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.183603Z"},"links":{"cited_paper":"/paper/2205.15127","citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:3e7323dd1bf6236b71ffb7cf0f96b3f5758f97e7fb52090c67a4033dfdf7a5df","observation_id":"965abd38-b443-4293-9d68-374e3f5f63cf","resolution":{"observed_at":"2026-08-15T19:24:57.183603Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.188886Z","title":"Simple and deep graph convolutional networks","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.188886Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:66f6d8a258aee3f6021c5b26c4ba8991ab8c85c05f0687f600935108ede6be37","observation_id":"79c4674c-593e-475d-96e1-95e868a8d056","resolution":{"observed_at":"2026-08-15T19:24:57.188886Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.193479Z","title":"Dynamical isometry and a mean field theory of rnns: Gating enables signal propagation in recurrent neural networks","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.193479Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:c96db83cf1ee038af742c8f5e5b9ec8ceb4ef0297119707fc2dc1aeaecb80ed6","observation_id":"8e9afcfb-5412-48e8-9353-a5f627c57b31","resolution":{"observed_at":"2026-08-15T19:24:57.193479Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.198105Z","title":"Bag of tricks for training deeper graph neural networks: A comprehensive benchmark study","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.198105Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:3ee8985107d3c498a158baf89b603f4f2bf18d49ca2f61d359c4c7184da8dd1d","observation_id":"3dfdf6cc-0ddc-4e71-b30b-c4f54f7ff572","resolution":{"observed_at":"2026-08-15T19:24:57.198105Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.203774Z","title":"On representing linear programs by graph neural networks","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.203774Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:37954fc6893e499547da658934adf880c1f48e7c2f4d4083e127528cc1006444","observation_id":"5fbbb7bd-41eb-44be-917b-88e0c5acc06e","resolution":{"observed_at":"2026-08-15T19:24:57.203774Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.208555Z","title":"Expressive power of graph neural networks for (mixed-integer) quadratic programs","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.208555Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:51dba1f465de83e1b463724cecdd77abcf41398a51e1340cfb08d210e560a882","observation_id":"41affb3d-1773-4747-ae36-d81d325aa57f","resolution":{"observed_at":"2026-08-15T19:24:57.208555Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.212766Z","title":"Adaptive universal generalized pagerank graph neural network","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.212766Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:3339363f8464f2b87b76f4e409d29bc27e350502518dee88d863112ef53fe12c","observation_id":"5d434ef5-ebd9-49b9-89bd-d47077b03b58","resolution":{"observed_at":"2026-08-15T19:24:57.212766Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.04895","last_updated":"2024-11-02T06:10:08Z","snapshot_observed_at":"2026-08-17T16:06:43.692790Z","submitted_at":"2024-08-09T06:46:06Z","title":"Better Not to Propagate: Understanding Edge Uncertainty and Over-smoothing in Signed Graph Neural Networks","version":3},"cited_work":{"arxiv_id":"2408.04895","doi":null,"metadata_source":"pith","pith_arxiv_id":"2408.04895","snapshot_observed_at":"2026-08-15T19:24:58.367439Z","title":"Better Not to Propagate: Understanding Edge Uncertainty and Over-smoothing in Signed Graph Neural Networks","venue":"cs.LG","work_id":"298880ad-51d9-4086-b946-d51d906fac24","year":2024},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.216873Z"},"links":{"cited_paper":"/paper/2408.04895","citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:a12a90ccb651d58e14336a1054a899316798e03672ebb6935c74dd699f24b47f","observation_id":"1c0385a8-57e2-49f1-9d77-398696756b45","resolution":{"observed_at":"2026-08-15T19:24:58.371978Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.221394Z","title":"Approximation algorithms for bin-packing—an updated survey","venue":null,"work_id":null,"year":1984},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.221394Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:be70a2285d76f6cde9c0d4829068c9034e9c9637ac834f02004c207d0693f0ef","observation_id":"7e08e607-16ac-4cb2-ac43-b0da85090cb6","resolution":{"observed_at":"2026-08-15T19:24:57.221394Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.225961Z","title":"On provable benefits of depth in training graph convolutional networks","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.225961Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:b46667920ca3085bff3603810af68db322f090009ba58e8e6cad46ec6dccdf31","observation_id":"2a89a58c-630a-4322-ac15-49342cf95d16","resolution":{"observed_at":"2026-08-15T19:24:57.225961Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.229567Z","title":"Metainit: Initializing learning by learning to initialize","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.229567Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:67291f3d627cf62165587e163c3d449bc443f8f57b2f51322e26305f47468829","observation_id":"ac92d969-592b-4c31-80e1-5b4af06ca604","resolution":{"observed_at":"2026-08-15T19:24:57.229567Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.10991","last_updated":"2023-09-06T06:58:11Z","snapshot_observed_at":"2026-08-16T22:36:36.439581Z","submitted_at":"2022-06-22T11:45:36Z","title":"Understanding convolution on graphs via energies","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.10991","snapshot_observed_at":"2026-08-15T19:24:57.233344Z","title":"Graph neural networks as gradient flows: Understanding graph convolutions via energy","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.233344Z"},"links":{"cited_paper":"/paper/2206.10991","citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:77bdc80164b79f577ba721d6e54759ecab6c990d8a63e098394697248af0ec43","observation_id":"60be6f7d-6686-4b3f-9be8-10d200ce0028","resolution":{"observed_at":"2026-08-15T19:24:57.233344Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.237971Z","title":"Adagnn: Graph neural networks with adaptive frequency response filter","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.237971Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:4ed0176e0907fab14b3874d68920741255f154272d1d149c4268a0437579428c","observation_id":"45e510d2-0f93-48ca-8eb6-7ad1df7b5c5c","resolution":{"observed_at":"2026-08-15T19:24:57.237971Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.241791Z","title":"Graph neural tangent kernel: Fusing graph neural networks with graph kernels","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.241791Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:64468ac269f25ffb77f46b4c5d388410f6b42bb21a0b233f61b72ba7e1ea698b","observation_id":"31e9114a-7cf5-420d-a082-f2f9cd8d781f","resolution":{"observed_at":"2026-08-15T19:24:57.241791Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.245697Z","title":"Learning from the dark: boosting graph convolutional neural networks with diverse negative samples","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.245697Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:7b95f9ed6f11070299f42c575e9aec00e35488044e3768817af35c64f81ff8aa","observation_id":"ac014f7d-d628-4c96-824c-075533f6ac38","resolution":{"observed_at":"2026-08-15T19:24:57.245697Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.249396Z","title":"Graph convolutional neural networks with diverse negative samples via decomposed determinant point processes","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.249396Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:e972f3c457e7f06f1a1b3ccb72cc7dcc56005e08916ec2564a58b0e0cfe369e0","observation_id":"8c1ad13c-9d26-4553-b872-484b09b846fb","resolution":{"observed_at":"2026-08-15T19:24:57.249396Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.253225Z","title":"Layer-diverse negative sampling for graph neural networks","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.253225Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:018962a6bf85a4131f1031ff38b72fb0f0c0e43091ef0f9dd10e2c8fdd97557e","observation_id":"621c0bb0-0d8d-45ec-bae7-84e219b48b18","resolution":{"observed_at":"2026-08-15T19:24:57.253225Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.257176Z","title":"The power of depth for feedforward neural networks","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.257176Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:52a1918654e3b9ff3f93c4a2a1c4ca7bf52c66e09bda9a2ac908be10f38bf4dd","observation_id":"3656e498-d58b-495e-a0e8-dc9caaab6ca5","resolution":{"observed_at":"2026-08-15T19:24:57.257176Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.260990Z","title":"DropMessage : Unifying random dropping for graph neural networks","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.260990Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:a37c73b9b38ded6d2d6d2e334e407da647653c5857458a873473c188588d5a25","observation_id":"e6ba5654-c20a-4c5a-8873-961247db9537","resolution":{"observed_at":"2026-08-15T19:24:57.260990Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.265198Z","title":"Grato: Graph neural network framework tackling over-smoothing with neural architecture search","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.265198Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:f78af3e5cbdc32c770d877b3474af47f288fd911ece4cefcfee3207e35b325ca","observation_id":"9adbaf49-5938-46a4-bcc1-b51b825b70cd","resolution":{"observed_at":"2026-08-15T19:24:57.265198Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.270059Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.270059Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:5b86ce5646ee2d1cdc87561a964dfd9d1d24c93d12f200b0d5e3eef26b021776","observation_id":"b86f95be-218a-49dc-8f7e-acd537e9302e","resolution":{"observed_at":"2026-08-15T19:24:57.270059Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.01267","last_updated":"2024-06-09T01:42:46Z","snapshot_observed_at":"2026-08-16T14:55:49.748264Z","submitted_at":"2023-10-02T15:08:52Z","title":"Cooperative Graph Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.01267","snapshot_observed_at":"2026-08-15T19:24:57.274279Z","title":"Cooperative graph neural networks","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.274279Z"},"links":{"cited_paper":"/paper/2310.01267","citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:24f2b991750126cdad0fe467ec2c6f764999136445748b416948499d5fb7d3e4","observation_id":"e108765b-40d5-4578-a6e0-39189c8262e9","resolution":{"observed_at":"2026-08-15T19:24:57.274279Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.278366Z","title":"Exact combinatorial optimization with graph convolutional neural networks","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.278366Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:ae5e9820a83eb9ff95d2c3ebf3e7b97147e3be1dcb2fede368b18aab1da7717a","observation_id":"09068c51-fd1a-46a4-8809-1e63687ecab6","resolution":{"observed_at":"2026-08-15T19:24:57.278366Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.283704Z","title":"Predict then propagate: Graph neural networks meet personalized pagerank","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.283704Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:904d9eba366b159c6ef67053bf6403cc58f52349270aa5bbab97aed7ba95d269","observation_id":"3651b512-aec5-43ea-af88-41d225370c0f","resolution":{"observed_at":"2026-08-15T19:24:57.283704Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.287779Z","title":"The travelling salesman problem and related problems","venue":null,"work_id":null,"year":1978},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.287779Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:ccc27c00a59df9543b075a0e5e61ad5da0f817672f2ba079f5d4e42f93375347","observation_id":"ac9e7fe6-52a9-4fac-a681-e6763f020b7c","resolution":{"observed_at":"2026-08-15T19:24:57.287779Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.291486Z","title":"Graph convolutional networks from the perspective of sheaves and the neural tangent kernel","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.291486Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:63a47a0b105fef79d922e06d9966ed290389d80f7e54498b69ad7081c0d09449","observation_id":"7eb00f4a-6ef1-484d-b288-3dddf1fdc8cd","resolution":{"observed_at":"2026-08-15T19:24:57.291486Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1901.08987","last_updated":"2019-05-24T01:10:22Z","snapshot_observed_at":"2026-08-14T17:25:50.300815Z","submitted_at":"2019-01-25T17:05:54Z","title":"Dynamical Isometry and a Mean Field Theory of LSTMs and GRUs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1901.08987","snapshot_observed_at":"2026-08-15T19:24:57.295398Z","title":"Dynamical isometry and a mean field theory of lstms and grus","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.295398Z"},"links":{"cited_paper":"/paper/1901.08987","citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:11ec7777d94e7fd7fa247a92ad604e4577de74cf51f20e147cd32b6268abb85f","observation_id":"6db5fc30-5048-4951-8af5-dbf317370da1","resolution":{"observed_at":"2026-08-15T19:24:57.295398Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.299939Z","title":"Understanding the difficulty of training deep feedforward neural networks","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.299939Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:741eb74780f8feb515d91c704b39986f521bfe11819d336ebdee3c2b35eb59c7","observation_id":"5ca7c281-f2a1-46b2-a255-f18ecc47f2a6","resolution":{"observed_at":"2026-08-15T19:24:57.299939Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.304468Z","title":"Orthogonal graph neural networks","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.304468Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:5ca7da15ed6d90cf5ccdb71109c1b0b7fd2461f74b14a0c684a1758866950d43","observation_id":"16ee122a-9cbd-4e15-a6bc-0351ecea1e7f","resolution":{"observed_at":"2026-08-15T19:24:57.304468Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.308345Z","title":"Contranorm: A contrastive learning perspective on oversmoothing and beyond","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.308345Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:229875de9d8f352b8b70af4cbf8f51abe02b90d29421757fd72bc0b5d5678af3","observation_id":"ef33b669-d633-4d61-a10f-4f30e59efe82","resolution":{"observed_at":"2026-08-15T19:24:57.308345Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.312707Z","title":"Structure-aware dropedge toward deep graph convolutional networks","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.312707Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:59a8701cb498d1a0c13152c8b95f4cee2e82c69f264e3cb83b4ce0724a764ec9","observation_id":"97ebbe23-c890-47a8-8329-806df2307d54","resolution":{"observed_at":"2026-08-15T19:24:57.312707Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.316760Z","title":"A gnn-guided predict-and-search framework for mixed-integer linear programming","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.316760Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:999a3b798b2c60bfe70cb8a45f66e5ee65d2dcf7e880c617d1c3d5379bb399ba","observation_id":"0c34132f-96b4-477f-b447-9a0317465f16","resolution":{"observed_at":"2026-08-15T19:24:57.316760Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.320668Z","title":"Mlpinit: Embarrassingly simple gnn training acceleration with mlp initialization","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.320668Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:99e6a57dc8b80f4841b12bc083301908ead0a4c059e553f362a84d07e24f32d7","observation_id":"ccbb12d0-971d-4dde-a8c3-234a8923b746","resolution":{"observed_at":"2026-08-15T19:24:57.320668Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.325302Z","title":"Which neural net architectures give rise to exploding and vanishing gradients? Advances in Neural Information Processing Systems, 31, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.325302Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:80c13ec61a5043f026f9cec0f589f0e2171a9f57ef49a8a2cb50e15de0f89af8","observation_id":"64449fb6-085d-422e-ae78-fe0b6fdf0e2a","resolution":{"observed_at":"2026-08-15T19:24:57.325302Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.329349Z","title":"Inequalities","venue":null,"work_id":null,"year":1952},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.329349Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:d517bde2c989fb38d23aa9ba6e5ee5cea017af55aaa196cce78a5d9d525aaea5","observation_id":"715c994a-279b-46f7-a3d0-be6cb559b44d","resolution":{"observed_at":"2026-08-15T19:24:57.329349Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.333143Z","title":"On the impact of the activation function on deep neural networks training","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.333143Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:4702104e86460b786c68cf1058ce56e8f5b250703c4421146343f72f4d11f92f","observation_id":"311bf995-afa0-4120-b21f-6564f9b3c9eb","resolution":{"observed_at":"2026-08-15T19:24:57.333143Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.337985Z","title":"The curse of depth in kernel regime","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.337985Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:9e53070c13bdb90cd2f4dd2b2dda5f32afb86598a646bed7fcea4489805d8603","observation_id":"c6c48ed9-fde9-4ac0-acd4-17611cabe764","resolution":{"observed_at":"2026-08-15T19:24:57.337985Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.342413Z","title":"Delving deep into rectifiers: Surpassing human-level performance on imagenet classification","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.342413Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:8737a974b53351d5c3c6b8ab53ac9e66a1a916371c774199eff5a54d08db35ad","observation_id":"7b8e1b15-bc81-4c63-a620-c8d7907dac51","resolution":{"observed_at":"2026-08-15T19:24:57.342413Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.346176Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.346176Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:d56a2f0b3c5a9d2c1625ea71954deb0be214d47052c4ece84089857652dcf870","observation_id":"52bb6147-a673-4cf7-b1d1-0a4db78ae4a4","resolution":{"observed_at":"2026-08-15T19:24:57.346176Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.349790Z","title":"Open graph benchmark: Datasets for machine learning on graphs","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.349790Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:05061ee759a4e6af6ee316cad470f25c29c5477fc7a0aa7474a743b2666068d9","observation_id":"ccb84385-0851-4a09-97b2-452c0dcc1451","resolution":{"observed_at":"2026-08-15T19:24:57.349790Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.353217Z","title":"Towards deepening graph neural networks: A gntk-based optimization perspective","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.353217Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:cf5681d7d5570682b465f661daffd8555f99c31caf0271804a9640ea2ccea212","observation_id":"971efa90-5585-45fa-ba8f-036325a644c2","resolution":{"observed_at":"2026-08-15T19:24:57.353217Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2008.09864","last_updated":"2022-07-09T01:41:01Z","snapshot_observed_at":"2026-08-07T03:30:20.016396Z","submitted_at":"2020-08-22T16:14:01Z","title":"Tackling Over-Smoothing for General Graph Convolutional Networks","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2008.09864","snapshot_observed_at":"2026-08-15T19:24:57.356848Z","title":"Tackling over-smoothing for general graph convolutional networks","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.356848Z"},"links":{"cited_paper":"/paper/2008.09864","citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:ae741f8cebad7904ab4b76009b9201ec72f930b2567c2531dd2e1a05b8cb025b","observation_id":"cdde0d09-08a9-4540-a33c-9b1ec448fe64","resolution":{"observed_at":"2026-08-15T19:24:57.356848Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.360839Z","title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.360839Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:8244e917f1636b1d06283a2b710f8115fcc99bd760e6f43ff8a6ddabeb9f8ed0","observation_id":"5a3b771d-48b2-48ef-a254-280b2b2710c5","resolution":{"observed_at":"2026-08-15T19:24:57.360839Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.364607Z","title":"Old can be gold: Better gradient flow can make vanilla-gcns great again","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.364607Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:d1df9af4822df405e985a95c4c46e10f4d82f5ca83ec1403fd5d243c3154234d","observation_id":"eacffcf3-3007-4e26-84ea-a62f40ed271f","resolution":{"observed_at":"2026-08-15T19:24:57.364607Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.368784Z","title":"Fast graph neural tangent kernel via kronecker sketching","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.368784Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:e3448a3827798a5058fbfce5c1568d790671c30de9b2dbddc59f3cfa7080e8cb","observation_id":"991eda1c-260d-41c0-8d87-d9d96af0ebd1","resolution":{"observed_at":"2026-08-15T19:24:57.368784Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.372827Z","title":"Towards feature overcorrelation in deeper graph neural networks","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.372827Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:13fa81c33a02af6574584eb25bdb8df9796aa441705f089b479c2ddea1b95dcd","observation_id":"f5ee31b8-3afb-45ae-b717-7671c4337684","resolution":{"observed_at":"2026-08-15T19:24:57.372827Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.377012Z","title":"Reducing oversmoothing in graph neural networks by changing the activation function","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.377012Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:c3116c048f2f69acb946c9145169ac45a24c416f4581ad4a7aa72c71a406b043","observation_id":"7af2a450-8f55-4c1d-a80b-f016c51c3ad6","resolution":{"observed_at":"2026-08-15T19:24:57.377012Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.23830","last_updated":"2024-10-31T11:21:20Z","snapshot_observed_at":"2026-08-17T15:05:39.960827Z","submitted_at":"2024-10-31T11:21:20Z","title":"Reducing Oversmoothing through Informed Weight Initialization in Graph Neural Networks","version":1},"cited_work":{"arxiv_id":"2410.23830","doi":null,"metadata_source":"pith","pith_arxiv_id":"2410.23830","snapshot_observed_at":"2026-08-15T19:24:58.295715Z","title":"Reducing Oversmoothing through Informed Weight Initialization in Graph Neural Networks","venue":"cs.LG","work_id":"e0a1af21-4d57-4360-a774-3303f59ad7bf","year":2024},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.380565Z"},"links":{"cited_paper":"/paper/2410.23830","citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:e5a3cb1b9c160c237954e388aa28b02a942bae8975bbe9986a77913e2bad1fae","observation_id":"8fd4e461-85c4-4018-99a1-9e3513db3c19","resolution":{"observed_at":"2026-08-15T19:24:58.301670Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.385708Z","title":"Not too little, not too much: a theoretical analysis of graph (over)smoothing","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.385708Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:5e5e59fef70a1930b19e1468f855082eb2cccae26dc19f2f4308b0a2fed93f6f","observation_id":"055a1984-9a77-40b8-bbe3-878948aebd87","resolution":{"observed_at":"2026-08-15T19:24:57.385708Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.389779Z","title":"Learning to branch in mixed integer programming","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.389779Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:a9a216165968e032cd30f1080b4d37e37175a7331a6c3f375d3a70ce2201c61b","observation_id":"4b8f2f51-81a8-4324-afa9-4f2892898b60","resolution":{"observed_at":"2026-08-15T19:24:57.389779Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.393959Z","title":"Learning to run heuristics in tree search","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.393959Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:fa4cce52a670ce1b1c47e71a7ee80aff87ddb1cc414ed3f24c66d00d3d45e516","observation_id":"10e52646-e61d-4d99-8a55-0379419e2458","resolution":{"observed_at":"2026-08-15T19:24:57.393959Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.398296Z","title":"Semi-supervised classification with graph convolutional networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.398296Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:d4448f552f0cb4cd25737275f711efb9af1b3438ab516c25be88d69034704bb1","observation_id":"449a0644-2b50-4eed-aa7b-94cc8762dac8","resolution":{"observed_at":"2026-08-15T19:24:57.398296Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.402705Z","title":"Goat: A global transformer on large-scale graphs","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.402705Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:d19c7ad2c1d2f2e116778600c3577da1a257d9ea638a56075be90b9de1a8339e","observation_id":"9ea9943b-5fd6-4908-91c6-4c765923f509","resolution":{"observed_at":"2026-08-15T19:24:57.402705Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.10808","last_updated":"2023-05-31T19:27:16Z","snapshot_observed_at":"2026-08-17T14:03:51.786463Z","submitted_at":"2023-01-25T19:52:58Z","title":"Graph Neural Tangent Kernel: Convergence on Large Graphs","version":2},"cited_work":{"arxiv_id":"2301.10808","doi":null,"metadata_source":"pith","pith_arxiv_id":"2301.10808","snapshot_observed_at":"2026-08-15T19:24:58.267552Z","title":"Graph Neural Tangent Kernel: Convergence on Large Graphs","venue":"cs.LG","work_id":"09e1a1bc-299d-4c01-9074-591c61318ee1","year":2023},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.406976Z"},"links":{"cited_paper":"/paper/2301.10808","citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:70ac9fec8b29b2e991fa5dba3150d0b3c2d9a92a709a69604da5c06ddc9cacd6","observation_id":"f48d6f55-59c6-4c3f-8c85-382115fb30ee","resolution":{"observed_at":"2026-08-15T19:24:58.272797Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.411653Z","title":"Imagenet classification with deep convolutional neural networks","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.411653Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:b010d389a3d8c740b6a028862a8c88cce376175d89eabd2616a0f39ef45ee8fc","observation_id":"d584834c-e796-4a60-869f-a899c0391e67","resolution":{"observed_at":"2026-08-15T19:24:57.411653Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.415906Z","title":"Efficient backprop","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":69,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.415906Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:103915ffe4f6e7671f388351fd5792037f8ac93598f3558d6b69d28067c9e60a","observation_id":"98cf3d30-fa0c-4697-a8e8-06282af42d26","resolution":{"observed_at":"2026-08-15T19:24:57.415906Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.419621Z","title":"Deep neural networks as gaussian processes","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":70,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.419621Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:ec94ff350491867098708ee91f2349636a4a012101610ad21431ef0cabaad7db","observation_id":"9e485507-1026-48fb-8827-8f99a8b9143a","resolution":{"observed_at":"2026-08-15T19:24:57.419621Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.01908","last_updated":"2024-06-06T09:07:37Z","snapshot_observed_at":"2026-08-16T13:46:28.797452Z","submitted_at":"2024-06-04T02:39:42Z","title":"PDHG-Unrolled Learning-to-Optimize Method for Large-Scale Linear Programming","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.01908","snapshot_observed_at":"2026-08-15T19:24:57.423732Z","title":"Pdhg-unrolled learning-to-optimize method for large-scale linear programming","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":71,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.423732Z"},"links":{"cited_paper":"/paper/2406.01908","citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:cc2149c9ba26afedd4523609b755b9d224a86d4ba37e2e4b474a87851145a14f","observation_id":"97b0ae74-eba1-43c8-b21b-d8d2188dd870","resolution":{"observed_at":"2026-08-15T19:24:57.423732Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.427844Z","title":"Deepgcns: Can gcns go as deep as cnns? In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":72,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.427844Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:4ec099ad0a32c21fb956d6ad56a6c0a5b61014a2f53adbb818c3ae96159482e2","observation_id":"f7d31d6d-eb62-4b9d-b16f-a2b5cdf8962a","resolution":{"observed_at":"2026-08-15T19:24:57.427844Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.431574Z","title":"Deepergcn: All you need to train deeper gcns, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":73,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.431574Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:0cb89b01e1b6455cb36c28c9a1a6c683de2ce2fd180fe42d7e42c17db09bdbab","observation_id":"ae96fa72-2d7c-4998-9627-6c52ce3475da","resolution":{"observed_at":"2026-08-15T19:24:57.431574Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.435829Z","title":"Training graph neural networks with 1000 layers","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":74,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.435829Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:cd579fd775a2fde44a8bd3e142e9818750a05e5b6539f1dbea2089f1e5b55e64","observation_id":"a5fdfd23-0b97-4ceb-bb15-4ad4704d2cfc","resolution":{"observed_at":"2026-08-15T19:24:57.435829Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.439693Z","title":"On the initialization of graph neural networks","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":75,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.439693Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:e4deb10ce6ba7e682fdce401e729788e59071813885abf1185f8473272b4c6c9","observation_id":"39d3aa15-7d9a-4622-83de-2c5806560381","resolution":{"observed_at":"2026-08-15T19:24:57.439693Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.443456Z","title":"On random deep weight-tied autoencoders: Exact asymptotic analysis, phase transitions, and implications to training","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":76,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.443456Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:97115a73ba897d62cc509759b9551d882b4d0ac5a83b2e1be4d65a8ed0d108db","observation_id":"464cce68-f87e-40bb-b6e2-5d73449364d0","resolution":{"observed_at":"2026-08-15T19:24:57.443456Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.447304Z","title":"On the power of small-size graph neural networks for linear programming","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":77,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.447304Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:5791203de28895d8fc0d10ac2c40bdf64633187e72afcf63fa0407d1c3fe2797","observation_id":"76dd95d7-44b1-4174-ba0a-cb8c4598a906","resolution":{"observed_at":"2026-08-15T19:24:57.447304Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:59.174624Z","title":"Deeper insights into graph convolutional networks for semi-supervised learning","venue":null,"work_id":"a90b88ff-def7-44fd-92ae-1aa62ff33a16","year":2018},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":78,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.450995Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:fabd37e40bdfaabfd67726d07438baa3407539638eefb477be9ee9b6e737f44d","observation_id":"05845693-62fe-47d8-92c7-ddde24a04d1d","resolution":{"observed_at":"2026-08-15T19:24:59.178707Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:59.161999Z","title":"Why deep neural networks for function approximation? In 5th International Conference on Learning Representations, ICLR 2017, 2017","venue":null,"work_id":"7e91c7d0-c88d-4c84-891a-316963a85add","year":2017},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":79,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.454790Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:0b8e9e273b13df87af6f08235757ba943ab851f726f8f9b7a0e279eb770cd113","observation_id":"919b764a-b3c9-46e2-a4d2-087d0d23dbe8","resolution":{"observed_at":"2026-08-15T19:24:59.166244Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:59.148740Z","title":"Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods","venue":null,"work_id":"b318de2d-22c9-40dd-b416-8b42782d237b","year":2021},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":80,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.459345Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:21d2cbce7322bebcb3a5779e7614fae5e48898ef1448eeac9c657667eb643958","observation_id":"69bc9f17-2224-4cdf-ab73-5bc399db6164","resolution":{"observed_at":"2026-08-15T19:24:59.152768Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:59.135787Z","title":"Towards deeper graph neural networks","venue":null,"work_id":"5a12c602-5b6c-4f91-906b-9ff2acccd8bc","year":2020},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":81,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.464185Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:ba1b3168fe2155746c1ebfcda08f4d1f5a02891fa727cc2de8a763ae582c5369","observation_id":"0aa74f5b-072e-432d-b9ee-2f58686f81d4","resolution":{"observed_at":"2026-08-15T19:24:59.140210Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2112.11628","last_updated":"2024-01-23T03:03:54Z","snapshot_observed_at":"2026-08-17T23:09:46.327006Z","submitted_at":"2021-12-22T02:18:31Z","title":"SkipNode: On Alleviating Performance Degradation for Deep Graph Convolutional Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.11628","snapshot_observed_at":"2026-08-15T19:24:57.469947Z","title":"Skipnode: On alleviating over-smoothing for deep graph convolutional networks","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":82,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.469947Z"},"links":{"cited_paper":"/paper/2112.11628","citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:6bab66489db0e321f270fbb0184a46913d09b97341a3ca1bdec08d6a342036b0","observation_id":"3c0f2f8f-95bf-4cfc-9315-b7a261e72da5","resolution":{"observed_at":"2026-08-15T19:24:57.469947Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:59.122548Z","title":"Break the ceiling: Stronger multi-scale deep graph convolutional networks","venue":null,"work_id":"df5d8082-c56f-470b-a467-43803a3d7dd0","year":2019},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":83,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.474318Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:f8cea7a9dcb42da6d83fbb7584bc7a16b94d05d0c4461e3d5a736986a8e3c305","observation_id":"55bff787-074d-4497-8d87-48a17e2c6dc2","resolution":{"observed_at":"2026-08-15T19:24:59.127095Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2008.08838","last_updated":"2023-11-03T22:30:21Z","snapshot_observed_at":"2026-08-14T20:40:10.175894Z","submitted_at":"2020-08-20T08:36:27Z","title":"Training Matters: Unlocking Potentials of Deeper Graph Convolutional Neural Networks","version":3},"cited_work":{"arxiv_id":"2008.08838","doi":null,"metadata_source":"pith","pith_arxiv_id":"2008.08838","snapshot_observed_at":"2026-08-15T19:24:58.221002Z","title":"Training Matters: Unlocking Potentials of Deeper Graph Convolutional Neural Networks","venue":"cs.LG","work_id":"35bda5da-6a19-43a9-add6-4307056c29fb","year":2020},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":84,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.478870Z"},"links":{"cited_paper":"/paper/2008.08838","citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:a0d0f961be3f6ac884615cbc4c8adcee52e96e1206661ceaafdb476a7f3404e8","observation_id":"97072f62-184b-4773-8dc4-803cd817ed81","resolution":{"observed_at":"2026-08-15T19:24:58.225419Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.08993","last_updated":"2024-10-28T09:03:11Z","snapshot_observed_at":"2026-08-16T20:19:48.235381Z","submitted_at":"2024-06-13T10:53:33Z","title":"Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.08993","snapshot_observed_at":"2026-08-15T19:24:57.483700Z","title":"Classic gnns are strong baselines: Reassessing gnns for node classification","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":85,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.483700Z"},"links":{"cited_paper":"/paper/2406.08993","citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:5230c25c07f44172f7b155ad04cd9c6eae557835cac734eba2ebf8ddf380c8d3","observation_id":"ef6dcf14-ddb0-4c6a-8159-5e464006715c","resolution":{"observed_at":"2026-08-15T19:24:57.483700Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:59.109090Z","title":"A fractional graph laplacian approach to oversmoothing","venue":null,"work_id":"b8d719bd-928e-4694-bdf1-f28a24bed1f5","year":2024},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":86,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.487586Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:642aaa18c6aa58ed608fe23facf35ad41789939fd539bd3723fbb2770ba2d909","observation_id":"0e48f098-bc44-4680-bb26-853e79e69d74","resolution":{"observed_at":"2026-08-15T19:24:59.113803Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:59.095015Z","title":"Gaussian process behaviour in wide deep neural networks","venue":null,"work_id":"8fef1880-3b65-469d-b474-02c3a4b0c37d","year":2018},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":87,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.491329Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:7a80adc31a5e307f9845ed3d8533989601e78d7ab82d0ced3623cd15811da04f","observation_id":"e773b079-42aa-4181-8859-1c0d318ec46a","resolution":{"observed_at":"2026-08-15T19:24:59.099939Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:59.080783Z","title":"Scattering gcn: Overcoming oversmoothness in graph convolutional networks","venue":null,"work_id":"91117010-459b-46f7-83e4-30d22ac2eb77","year":2020},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":88,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.496458Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:3dbaa0c270418db651aa3a169d8d9a7f3fdef92bc9ade58bc339e42d19ec9471","observation_id":"3ba60682-76e3-47f0-b552-2ade60841c2d","resolution":{"observed_at":"2026-08-15T19:24:59.085480Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2012.13349","last_updated":"2021-07-29T13:41:25Z","snapshot_observed_at":"2026-08-16T18:56:28.610181Z","submitted_at":"2020-12-23T09:33:11Z","title":"Solving Mixed Integer Programs Using Neural Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2012.13349","snapshot_observed_at":"2026-08-15T19:24:57.500704Z","title":"Solving mixed integer programs using neural networks","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":89,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.500704Z"},"links":{"cited_paper":"/paper/2012.13349","citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:d518888817e18645abccb90ed3ddcd1093278ebbac136457fd1eb8802f9d0140","observation_id":"0861386b-bead-4f09-927c-a1a06e218e10","resolution":{"observed_at":"2026-08-15T19:24:57.500704Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:59.065446Z","title":"Bayesian learning for neural networks, volume 118","venue":null,"work_id":"9fc14e21-4648-4b25-bc4d-6f47f2b38a3b","year":1996},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":90,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.505484Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:3cc3ebde33878d91d456388f3cd6ae5216ff03172b4cc99e08ac11c323cbe7a3","observation_id":"0d56b641-d36f-4fd3-bbb5-783773e5d91a","resolution":{"observed_at":"2026-08-15T19:24:59.070836Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.510675Z","title":"Random gradient-free minimization of convex functions","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":91,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.510675Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:8bf45ca53e8406b3bc2464b6f5f6cacb8c2d75a86228298d21d0ab2ee27b8c0d","observation_id":"0ffdf19a-14d5-45ae-bb38-b5a372448059","resolution":{"observed_at":"2026-08-15T19:24:57.510675Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:57.516955Z","title":"Revisiting over-smoothing and over-squashing using ollivier-ricci curvature","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":92,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.516955Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:2cbe73d02d4612e3bfdddc26b4a120b72ade39ea49facbcf3db5fc4faead60d5","observation_id":"d1029184-dd79-4dfc-9fbf-c5cb31389c97","resolution":{"observed_at":"2026-08-15T19:24:57.516955Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:59.035739Z","title":"Graph neural networks exponentially lose expressive power for node classification","venue":null,"work_id":"778bfe8e-a313-4ee2-beac-e6993df051af","year":2019},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":93,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.521664Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:b7a34883a6f5a7e4f7f74baae08262ee941eaeeda64d56c280e36ab9627f4142","observation_id":"d1a39ea9-c899-495c-b66e-7e8fb88b1524","resolution":{"observed_at":"2026-08-15T19:24:59.040403Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:59.022666Z","title":"Applications of combinatorial optimization","venue":null,"work_id":"80e7bb47-59a6-434d-835c-fcee384d0bcb","year":2014},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":94,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.527478Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:a86ba4507dca149a6c475d1e15e43ce62e231dcd9a832f4244dd5d9b2278c438","observation_id":"2a243edb-a5e1-4e6d-b4ef-4aa14094aac5","resolution":{"observed_at":"2026-08-15T19:24:59.026991Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:59.008324Z","title":"Resurrecting the sigmoid in deep learning through dynamical isometry: Theory and practice","venue":null,"work_id":"8d82d04b-1e8e-43b2-a364-2841878cfc46","year":2017},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":95,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.531659Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:44b7cc40145f25a106d18271e95e77c9d34d11bc6b8f087f41483713a4c7972c","observation_id":"acf57eee-1117-4469-8360-e89e9ab3e866","resolution":{"observed_at":"2026-08-15T19:24:59.013044Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:58.993064Z","title":"The emergence of spectral universality in deep networks","venue":null,"work_id":"4843c57f-b519-4b40-9981-6d42d7ac42e7","year":1924},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":96,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.536485Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:81bdfefa9407621b04d9e072b0121179e0a5ba5c142ee22180f8efcd230ad8fc","observation_id":"caece68e-d163-45a7-b450-4279ce076e23","resolution":{"observed_at":"2026-08-15T19:24:58.998997Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.11640","last_updated":"2024-03-02T21:17:13Z","snapshot_observed_at":"2026-08-16T15:53:25.866662Z","submitted_at":"2023-02-22T20:32:59Z","title":"A critical look at the evaluation of GNNs under heterophily: Are we really making progress?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.11640","snapshot_observed_at":"2026-08-15T19:24:57.540519Z","title":"A critical look at the evaluation of gnns under heterophily: Are we really making progress? arXiv preprint arXiv:2302.11640, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":97,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.540519Z"},"links":{"cited_paper":"/paper/2302.11640","citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:370a997a38a17c82100a28f286cc14290cdf0f7606f1a73380328b373d714210","observation_id":"79abcf6a-45fc-494f-8f69-f65c71132e3a","resolution":{"observed_at":"2026-08-15T19:24:57.540519Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:58.975607Z","title":"Exponential expressivity in deep neural networks through transient chaos","venue":null,"work_id":"9e537a2c-4cb6-45bc-8e04-2715a412771a","year":2016},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":98,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.545448Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:56a04ab98cc0fea3454acee36c3a004c8c27f5092f652f57c9e0e5c6cadb7b45","observation_id":"64916308-d86e-4cb9-9ca8-94efebc62be7","resolution":{"observed_at":"2026-08-15T19:24:58.983544Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:58.956849Z","title":"Exploring the power of graph neural networks in solving linear optimization problems","venue":null,"work_id":"ad04c555-6624-4da6-aebc-858f70e4b80f","year":2024},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":99,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.549735Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:45582d1bb72a7ee583bc57e0b3cbfe4f3be2c9915d27dc23e64d602ff7dfa8bd","observation_id":"8c0266ee-4fec-4b6c-8afa-2482d1c8af11","resolution":{"observed_at":"2026-08-15T19:24:58.961682Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:24:58.942530Z","title":"Dropedge: Towards deep graph convolutional networks on node classification","venue":null,"work_id":"e5c6f128-6e15-40ca-a8ce-df45f8e0e7b8","year":2020},"citing_paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective","version":2},"reference_index":100,"source":"arxiv_source","source_observed_at":"2026-08-15T19:24:57.553636Z"},"links":{"citing_paper":"/paper/2506.16790"},"observation_digest":"sha256:045e3438b017ff775833301f342182c59cc0f79a0ae48e15de289cab8965de87","observation_id":"3f34df87-c140-425a-b4e8-f5be47f3f8a6","resolution":{"observed_at":"2026-08-15T19:24:58.947191Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.16790","last_updated":"2025-07-15T05:21:45Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-17T14:07:51.697114Z","submitted_at":"2025-06-20T07:14:31Z","title":"Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":80,"verified_exact":4,"verified_fuzzy":16},"total_outbound_references":147},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 100 of 147 outbound references and 0 inbound Pith citation observations for arXiv:2506.16790."}