{"as_of":"2026-08-09T21:34:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f25d4c57947bc7466925013d729320d90eb8e0b6951659c29b841bb983ccc994","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":18,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":18,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":18,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":18,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T15:23:03.372461Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-04T13:59:52.011828Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2004.11198","last_updated":"2020-11-03T19:20:22Z","snapshot_observed_at":"2026-07-06T09:14:40.956312Z","submitted_at":"2020-04-23T14:46:10Z","title":"SIGN: Scalable Inception Graph Neural Networks","version":3},"cited_work":{"arxiv_id":"2004.11198","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2004.11198","snapshot_observed_at":"2026-07-04T13:59:52.011828Z","title":"Sign: Scalable inception graph neural networks","venue":null,"work_id":"13b3af16-9208-470c-9c5f-892cfdfd657d","year":2004},"citing_paper":{"arxiv_id":"2006.10637","last_updated":"2020-10-09T11:39:32Z","snapshot_observed_at":"2026-08-07T10:11:23.671536Z","submitted_at":"2020-06-18T16:06:18Z","title":"Temporal Graph Networks for Deep Learning on Dynamic Graphs","version":3},"reference_index":140,"source":"arxiv_source","source_observed_at":"2026-05-17T17:04:50.111090Z"},"links":{"cited_paper":"/paper/2004.11198","citing_paper":"/paper/2006.10637"},"observation_digest":"sha256:bad540d06bb7991e7bf1baee9a01efb0402ec6a2be8e61ed97048799403eeb58","observation_id":"c52c7df0-2570-4f66-9675-c0a945d5d696","resolution":{"observed_at":"2026-05-17T17:04:50.317160Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2004.11198","last_updated":"2020-11-03T19:20:22Z","snapshot_observed_at":"2026-07-06T09:14:40.956312Z","submitted_at":"2020-04-23T14:46:10Z","title":"SIGN: Scalable Inception Graph Neural Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.11198","snapshot_observed_at":"2026-08-07T15:23:03.372461Z","title":"Sign: Scalable inception graph neural networks","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2505.15405","last_updated":"2026-06-03T19:53:11Z","snapshot_observed_at":"2026-08-07T15:16:07.374027Z","submitted_at":"2025-05-21T11:47:40Z","title":"HOPSE: Scalable Higher-Order Positional and Structural Encoder for Combinatorial Representations","version":3},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-07T15:23:03.372461Z"},"links":{"cited_paper":"/paper/2004.11198","citing_paper":"/paper/2505.15405"},"observation_digest":"sha256:064e3324ec326e880511da1e05378d603b7a095a9615214e63bf298a655a0c65","observation_id":"744996fe-56e6-4710-a695-c5dc9993122e","resolution":{"observed_at":"2026-08-07T15:23:03.372461Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2004.11198","last_updated":"2020-11-03T19:20:22Z","snapshot_observed_at":"2026-07-06T09:14:40.956312Z","submitted_at":"2020-04-23T14:46:10Z","title":"SIGN: Scalable Inception Graph Neural Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.11198","snapshot_observed_at":"2026-08-07T05:00:55.485694Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.09234","last_updated":"2025-06-10T20:42:41Z","snapshot_observed_at":"2026-08-09T19:25:31.956338Z","submitted_at":"2025-06-10T20:42:41Z","title":"Transaction Categorization with Relational Deep Learning in QuickBooks","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T05:00:55.485694Z"},"links":{"cited_paper":"/paper/2004.11198","citing_paper":"/paper/2506.09234"},"observation_digest":"sha256:b6b395834d8b36eb2c694b7810af6581064c8b37435f7db4b93422b195a001c9","observation_id":"670a69d7-58ca-47b5-8e1a-f783f6c50f32","resolution":{"observed_at":"2026-08-07T05:00:55.485694Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2004.11198","last_updated":"2020-11-03T19:20:22Z","snapshot_observed_at":"2026-07-06T09:14:40.956312Z","submitted_at":"2020-04-23T14:46:10Z","title":"SIGN: Scalable Inception Graph Neural Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.11198","snapshot_observed_at":"2026-08-07T00:42:11.097556Z","title":"Sign: Scalable inception graph neural networks,","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2506.13083","last_updated":"2025-06-16T03:59:38Z","snapshot_observed_at":"2026-08-09T18:30:06.013695Z","submitted_at":"2025-06-16T03:59:38Z","title":"Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T00:42:11.097556Z"},"links":{"cited_paper":"/paper/2004.11198","citing_paper":"/paper/2506.13083"},"observation_digest":"sha256:a62f8a56ffb8591235383e6d0e774ea5dd30735b64d5d0e72d1bca75d31ac22e","observation_id":"05abfcd5-b55b-4272-932e-6d241e48c083","resolution":{"observed_at":"2026-08-07T00:42:11.097556Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2004.11198","last_updated":"2020-11-03T19:20:22Z","snapshot_observed_at":"2026-07-06T09:14:40.956312Z","submitted_at":"2020-04-23T14:46:10Z","title":"SIGN: Scalable Inception Graph Neural Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.11198","snapshot_observed_at":"2026-08-06T14:43:05.350087Z","title":"Rossi, F","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.18219","last_updated":"2026-06-22T15:04:58Z","snapshot_observed_at":"2026-08-09T06:50:19.745206Z","submitted_at":"2025-07-24T09:15:07Z","title":"FedSA-GCL: A Semi-Asynchronous Federated Graph Learning Framework with Personalized Aggregation and Cluster-Aware Broadcasting","version":3},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T14:43:05.350087Z"},"links":{"cited_paper":"/paper/2004.11198","citing_paper":"/paper/2507.18219"},"observation_digest":"sha256:73526997d8f92001944035fc5fb42a30dc043f42678acdb8d6904d5946189079","observation_id":"7b604f70-ce36-4a38-bf11-74ce36a82ca5","resolution":{"observed_at":"2026-08-06T14:43:05.350087Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2004.11198","last_updated":"2020-11-03T19:20:22Z","snapshot_observed_at":"2026-07-06T09:14:40.956312Z","submitted_at":"2020-04-23T14:46:10Z","title":"SIGN: Scalable Inception Graph Neural Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.11198","snapshot_observed_at":"2026-08-03T19:22:02.356335Z","title":"Sign: Scalable inception graph neural networks","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2512.01113","last_updated":"2026-07-13T20:34:04Z","snapshot_observed_at":"2026-08-07T11:25:32.057107Z","submitted_at":"2025-11-30T22:19:55Z","title":"Efficiently Learning Branching Networks for Multitask Algorithmic Reasoning","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-03T19:22:02.356335Z"},"links":{"cited_paper":"/paper/2004.11198","citing_paper":"/paper/2512.01113"},"observation_digest":"sha256:0b2971a55036ddd93c5c1786ee43eb4ce346080a65f2d1baac7aa38c00e7f04c","observation_id":"ce30729c-07e2-4ea5-8c24-15cbe411c421","resolution":{"observed_at":"2026-08-03T19:22:02.356335Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2004.11198","last_updated":"2020-11-03T19:20:22Z","snapshot_observed_at":"2026-07-06T09:14:40.956312Z","submitted_at":"2020-04-23T14:46:10Z","title":"SIGN: Scalable Inception Graph Neural Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.11198","snapshot_observed_at":"2026-08-02T23:33:31.322398Z","title":"SIGN: Scalable inception graph neural networks.arXiv preprint arXiv:2004.11198,","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2602.13697","last_updated":"2026-06-04T09:11:11Z","snapshot_observed_at":"2026-08-09T09:08:14.315139Z","submitted_at":"2026-02-14T09:38:57Z","title":"No Need to Train Your RDB Foundation Model","version":2},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-02T23:33:31.322398Z"},"links":{"cited_paper":"/paper/2004.11198","citing_paper":"/paper/2602.13697"},"observation_digest":"sha256:dddf4b468727fc8f156957348367563d237e39cbac2b57d33bed481af7f3a4a4","observation_id":"0b261f86-2fcc-4b8d-ae9e-3aa2bc01813b","resolution":{"observed_at":"2026-08-02T23:33:31.322398Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2004.11198","last_updated":"2020-11-03T19:20:22Z","snapshot_observed_at":"2026-07-06T09:14:40.956312Z","submitted_at":"2020-04-23T14:46:10Z","title":"SIGN: Scalable Inception Graph Neural Networks","version":3},"cited_work":{"arxiv_id":"2004.11198","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2004.11198","snapshot_observed_at":"2026-07-04T13:59:52.011828Z","title":"Sign: Scalable inception graph neural networks","venue":null,"work_id":"13b3af16-9208-470c-9c5f-892cfdfd657d","year":2004},"citing_paper":{"arxiv_id":"2602.17071","last_updated":"2026-04-11T12:06:12Z","snapshot_observed_at":"2026-08-01T04:28:59.324747Z","submitted_at":"2026-02-19T04:26:57Z","title":"AdvSynGNN: Structure-Adaptive Graph Neural Nets via Adversarial Synthesis and Self-Corrective Propagation","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-15T21:30:43.925179Z"},"links":{"cited_paper":"/paper/2004.11198","citing_paper":"/paper/2602.17071"},"observation_digest":"sha256:c7a91a5844d763224bf23688bfb922c0f15140719b6b2ac090712595489e07ac","observation_id":"fffc67d1-e249-4c68-9a07-a1e403f50d7f","resolution":{"observed_at":"2026-05-15T21:31:39.367252Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2004.11198","last_updated":"2020-11-03T19:20:22Z","snapshot_observed_at":"2026-07-06T09:14:40.956312Z","submitted_at":"2020-04-23T14:46:10Z","title":"SIGN: Scalable Inception Graph Neural Networks","version":3},"cited_work":{"arxiv_id":"2004.11198","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2004.11198","snapshot_observed_at":"2026-07-04T13:59:52.011828Z","title":"Sign: Scalable inception graph neural networks","venue":null,"work_id":"13b3af16-9208-470c-9c5f-892cfdfd657d","year":2004},"citing_paper":{"arxiv_id":"2604.22676","last_updated":"2026-04-28T11:17:49Z","snapshot_observed_at":"2026-08-03T20:53:27.918660Z","submitted_at":"2026-04-24T16:00:53Z","title":"Operational Feature Fingerprints of Graph Datasets via a White-Box Signal-Subspace Probe","version":2},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-05-08T12:10:02.120989Z"},"links":{"cited_paper":"/paper/2004.11198","citing_paper":"/paper/2604.22676"},"observation_digest":"sha256:0c23337698c09840330e7d2a9cbc09f179904864e174901d318b87941f8ea41f","observation_id":"5b838abc-1e25-4e2a-a6f9-5c9226d88de8","resolution":{"observed_at":"2026-05-11T19:21:08.636335Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2004.11198","last_updated":"2020-11-03T19:20:22Z","snapshot_observed_at":"2026-07-06T09:14:40.956312Z","submitted_at":"2020-04-23T14:46:10Z","title":"SIGN: Scalable Inception Graph Neural Networks","version":3},"cited_work":{"arxiv_id":"2004.11198","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2004.11198","snapshot_observed_at":"2026-07-04T13:59:52.011828Z","title":"Sign: Scalable inception graph neural networks","venue":null,"work_id":"13b3af16-9208-470c-9c5f-892cfdfd657d","year":2004},"citing_paper":{"arxiv_id":"2605.01484","last_updated":"2026-05-02T15:11:52Z","snapshot_observed_at":"2026-08-02T04:58:06.359776Z","submitted_at":"2026-05-02T15:11:52Z","title":"Evaluating LLMs on Large-Scale Graph Property Estimation via Random Walks","version":1},"reference_index":166,"source":"arxiv_source","source_observed_at":"2026-05-09T15:09:03.417040Z"},"links":{"cited_paper":"/paper/2004.11198","citing_paper":"/paper/2605.01484"},"observation_digest":"sha256:d6b7cc7ffa47b36168617fefcda67b46de2e8f54ce031fd21115833c73fb0fec","observation_id":"e6ed7911-fe34-4cde-b12e-436b4ddf018e","resolution":{"observed_at":"2026-05-11T16:46:06.586336Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2004.11198","last_updated":"2020-11-03T19:20:22Z","snapshot_observed_at":"2026-07-06T09:14:40.956312Z","submitted_at":"2020-04-23T14:46:10Z","title":"SIGN: Scalable Inception Graph Neural Networks","version":3},"cited_work":{"arxiv_id":"2004.11198","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2004.11198","snapshot_observed_at":"2026-07-04T13:59:52.011828Z","title":"Sign: Scalable inception graph neural networks","venue":null,"work_id":"13b3af16-9208-470c-9c5f-892cfdfd657d","year":2004},"citing_paper":{"arxiv_id":"2605.10975","last_updated":"2026-05-08T22:35:17Z","snapshot_observed_at":"2026-07-06T23:22:52.369277Z","submitted_at":"2026-05-08T22:35:17Z","title":"Hierarchical Multi-Scale Graph Neural Networks: Scalable Heterophilous Learning with Oversmoothing and Oversquashing Mitigation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-13T05:57:17.440810Z"},"links":{"cited_paper":"/paper/2004.11198","citing_paper":"/paper/2605.10975"},"observation_digest":"sha256:dfe48e0a59acd6e0969dcca554df6c59b8f82272a967dbd638a7deb176427741","observation_id":"a90ef881-6c91-432e-9905-71e507614c31","resolution":{"observed_at":"2026-05-13T05:57:22.406311Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2004.11198","last_updated":"2020-11-03T19:20:22Z","snapshot_observed_at":"2026-07-06T09:14:40.956312Z","submitted_at":"2020-04-23T14:46:10Z","title":"SIGN: Scalable Inception Graph Neural Networks","version":3},"cited_work":{"arxiv_id":"2004.11198","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2004.11198","snapshot_observed_at":"2026-07-04T13:59:52.011828Z","title":"Sign: Scalable inception graph neural networks","venue":null,"work_id":"13b3af16-9208-470c-9c5f-892cfdfd657d","year":2004},"citing_paper":{"arxiv_id":"2605.11468","last_updated":"2026-05-12T03:32:54Z","snapshot_observed_at":"2026-07-06T23:23:16.461539Z","submitted_at":"2026-05-12T03:32:54Z","title":"CAMPA: Efficient and Aligned Multimodal Graph Learning via Decoupled Propagation and Aggregation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-13T01:52:23.038362Z"},"links":{"cited_paper":"/paper/2004.11198","citing_paper":"/paper/2605.11468"},"observation_digest":"sha256:9029d5fcabcf87cc29a13577b41d6120ee9037d402d3dad2a3abfe085b9fafa9","observation_id":"ee4d57c1-d5d9-4beb-b4b6-e0420c551a61","resolution":{"observed_at":"2026-05-13T01:57:06.236741Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2004.11198","last_updated":"2020-11-03T19:20:22Z","snapshot_observed_at":"2026-07-06T09:14:40.956312Z","submitted_at":"2020-04-23T14:46:10Z","title":"SIGN: Scalable Inception Graph Neural Networks","version":3},"cited_work":{"arxiv_id":"2004.11198","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2004.11198","snapshot_observed_at":"2026-07-04T13:59:52.011828Z","title":"Sign: Scalable inception graph neural networks","venue":null,"work_id":"13b3af16-9208-470c-9c5f-892cfdfd657d","year":2004},"citing_paper":{"arxiv_id":"2605.25111","last_updated":"2026-05-24T14:49:46Z","snapshot_observed_at":"2026-07-06T23:35:06.733347Z","submitted_at":"2026-05-24T14:49:46Z","title":"Revisiting Pre-Propagation GNNs: Robust Diffusion Operators and Hidden-State Re-Propagation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-30T12:07:38.327721Z"},"links":{"cited_paper":"/paper/2004.11198","citing_paper":"/paper/2605.25111"},"observation_digest":"sha256:068b351d27c0148d4608c0d1048ddcd9a4859d50973dd9d789daabcc9744d3f7","observation_id":"9408a24b-4833-45da-bd2f-494548ddb210","resolution":{"observed_at":"2026-06-30T12:34:39.565165Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2004.11198","last_updated":"2020-11-03T19:20:22Z","snapshot_observed_at":"2026-07-06T09:14:40.956312Z","submitted_at":"2020-04-23T14:46:10Z","title":"SIGN: Scalable Inception Graph Neural Networks","version":3},"cited_work":{"arxiv_id":"2004.11198","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2004.11198","snapshot_observed_at":"2026-07-04T13:59:52.011828Z","title":"Sign: Scalable inception graph neural networks","venue":null,"work_id":"13b3af16-9208-470c-9c5f-892cfdfd657d","year":2004},"citing_paper":{"arxiv_id":"2606.01660","last_updated":"2026-06-01T04:14:13Z","snapshot_observed_at":"2026-08-02T14:22:41.251311Z","submitted_at":"2026-06-01T04:14:13Z","title":"Gate the Filter, Not the Message: Node-Channel Mixtures for Pre-Propagation GNNs","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-28T15:36:27.182300Z"},"links":{"cited_paper":"/paper/2004.11198","citing_paper":"/paper/2606.01660"},"observation_digest":"sha256:8b58a63b23c8e6905bf0a8438092d73bb1a2337286fa5503ab5564bd346da605","observation_id":"2a6d1aa4-8ede-46dd-a7d8-4aec3a1b44d7","resolution":{"observed_at":"2026-07-01T22:16:16.210046Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2004.11198","last_updated":"2020-11-03T19:20:22Z","snapshot_observed_at":"2026-07-06T09:14:40.956312Z","submitted_at":"2020-04-23T14:46:10Z","title":"SIGN: Scalable Inception Graph Neural Networks","version":3},"cited_work":{"arxiv_id":"2004.11198","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2004.11198","snapshot_observed_at":"2026-07-04T13:59:52.011828Z","title":"Sign: Scalable inception graph neural networks","venue":null,"work_id":"13b3af16-9208-470c-9c5f-892cfdfd657d","year":2004},"citing_paper":{"arxiv_id":"2606.22429","last_updated":"2026-06-21T10:40:33Z","snapshot_observed_at":"2026-08-07T18:47:13.612767Z","submitted_at":"2026-06-21T10:40:33Z","title":"Enhancing LLMs for Graph Tasks via Graph-aware LoRA Generation","version":1},"reference_index":131,"source":"arxiv_source","source_observed_at":"2026-06-26T10:59:25.867813Z"},"links":{"cited_paper":"/paper/2004.11198","citing_paper":"/paper/2606.22429"},"observation_digest":"sha256:be5bb5d2a1aab1e335a9d526f4a6d66cd2486a7eab51c6333073ba491d6fba3b","observation_id":"8b0c151a-9891-4892-a309-252f9f56080d","resolution":{"observed_at":"2026-07-04T08:49:41.986158Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2004.11198","last_updated":"2020-11-03T19:20:22Z","snapshot_observed_at":"2026-07-06T09:14:40.956312Z","submitted_at":"2020-04-23T14:46:10Z","title":"SIGN: Scalable Inception Graph Neural Networks","version":3},"cited_work":{"arxiv_id":"2004.11198","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2004.11198","snapshot_observed_at":"2026-07-04T13:59:52.011828Z","title":"Sign: Scalable inception graph neural networks","venue":null,"work_id":"13b3af16-9208-470c-9c5f-892cfdfd657d","year":2004},"citing_paper":{"arxiv_id":"2606.26664","last_updated":"2026-06-25T06:55:06Z","snapshot_observed_at":"2026-08-03T15:42:50.109793Z","submitted_at":"2026-06-25T06:55:06Z","title":"TGHE: Template-based Graph Homomorphic Encryption for Privacy-Preserving GNN Inference in Edge-Cloud Systems","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-26T04:45:51.759131Z"},"links":{"cited_paper":"/paper/2004.11198","citing_paper":"/paper/2606.26664"},"observation_digest":"sha256:3aa1e8b619fc524763ea1b0efd03988137a63d72f38125c0bcf9098cd2e7de7a","observation_id":"e1bb63d6-152a-4c91-939f-63d67184aca1","resolution":{"observed_at":"2026-07-04T13:59:52.013544Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2004.11198","last_updated":"2020-11-03T19:20:22Z","snapshot_observed_at":"2026-07-06T09:14:40.956312Z","submitted_at":"2020-04-23T14:46:10Z","title":"SIGN: Scalable Inception Graph Neural Networks","version":3},"cited_work":{"arxiv_id":"2004.11198","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2004.11198","snapshot_observed_at":"2026-07-04T13:59:52.011828Z","title":"Sign: Scalable inception graph neural networks","venue":null,"work_id":"13b3af16-9208-470c-9c5f-892cfdfd657d","year":2004},"citing_paper":{"arxiv_id":"2606.32016","last_updated":"2026-06-30T17:47:39Z","snapshot_observed_at":"2026-07-07T00:05:37.068846Z","submitted_at":"2026-06-30T17:47:39Z","title":"FedLAB: Traceable Semantic Codebooks for Federated Multimodal Graph Foundation Learning","version":1},"reference_index":116,"source":"arxiv_source","source_observed_at":"2026-07-01T06:10:26.634933Z"},"links":{"cited_paper":"/paper/2004.11198","citing_paper":"/paper/2606.32016"},"observation_digest":"sha256:44cf06b14c2bc00b7e39d52d5ad45f06483c3178d08cd780e3d573d999ca36f0","observation_id":"522479d6-d8ba-4be8-b9d5-a2bab1414a8d","resolution":{"observed_at":"2026-07-01T09:45:40.654839Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2004.11198","last_updated":"2020-11-03T19:20:22Z","snapshot_observed_at":"2026-07-06T09:14:40.956312Z","submitted_at":"2020-04-23T14:46:10Z","title":"SIGN: Scalable Inception Graph Neural Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.11198","snapshot_observed_at":"2026-07-14T09:09:45.167581Z","title":"Frasca, E","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2607.10804","last_updated":"2026-07-12T15:23:38Z","snapshot_observed_at":"2026-08-09T13:32:21.348905Z","submitted_at":"2026-07-12T15:23:38Z","title":"When does distribution shift break graph neural networks calibration?","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-07-14T09:09:45.167581Z"},"links":{"cited_paper":"/paper/2004.11198","citing_paper":"/paper/2607.10804"},"observation_digest":"sha256:ad6ddb6a2ad9249bd00f35f02af57c0de9ec3ba1d36940eafefe3869073ff2db","observation_id":"a43f6185-727d-4ab1-be0d-4bc8378ee071","resolution":{"observed_at":"2026-07-14T09:09:45.167581Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2004.11198/citation-record","integrity":"/paper/2004.11198/integrity","json":"/paper/2004.11198/citation-record.json","paper":"/paper/2004.11198"},"outbound":[],"paper":{"arxiv_id":"2004.11198","last_updated":"2020-11-03T19:20:22Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T09:14:40.956312Z","submitted_at":"2020-04-23T14:46:10Z","title":"SIGN: Scalable Inception Graph Neural Networks"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 18 inbound Pith citation observations for arXiv:2004.11198."}