{"as_of":"2026-08-19T09:31:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:05b4ecd4378fcdfa0861cb1e04b5dade83777c255eb636a1c5568e241adab7d0","coverage":[{"denominator":47,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":47,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T21:10:54.478962Z","state":"measured"},{"denominator":47,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":47,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+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/2501.07598/citation-record","integrity":"/paper/2501.07598/integrity","json":"/paper/2501.07598/citation-record.json","paper":"/paper/2501.07598"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:10:55.543694Z","title":"Deep collective classification in heterogeneous information networks,","venue":null,"work_id":"f2bac4fc-c708-4380-bca8-58a94cea403a","year":2018},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.126323Z"},"links":{"citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:75aa7ec071d7db82907bb142337785bc8fc06c34b0294e3b66d6149ede950f78","observation_id":"2a528460-d23b-4a58-b639-05ef4377024a","resolution":{"observed_at":"2026-08-10T21:10:55.549410Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:10:55.525226Z","title":"Dropedge: Towards deep graph convolutional networks on node classification,","venue":null,"work_id":"6753d5ad-9e98-40c4-9c59-f0fbb4544f56","year":2019},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.131792Z"},"links":{"citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:7584458023c521512c70902183d5e1397b86e8e83bac3c44d6200ddad30a3745","observation_id":"b21b5c67-9a47-405e-aec0-0c32ee871254","resolution":{"observed_at":"2026-08-10T21:10:55.531220Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:10:55.503707Z","title":"Node classification in signed social networks,","venue":null,"work_id":"fd388418-805d-45bd-bace-49e02b3f4dab","year":2016},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.137277Z"},"links":{"citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:7361f24e4b16505e15e43df60b563c2bae3c63c9c9ec6512e9be52b3a2c065a0","observation_id":"7fecfc49-147a-4fc3-b682-06b666a9f723","resolution":{"observed_at":"2026-08-10T21:10:55.511133Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:10:55.480707Z","title":"Link prediction based on graph neu- ral networks,","venue":null,"work_id":"493c7026-b1d9-453e-ae7a-2349e57ccec7","year":2018},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.142206Z"},"links":{"citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:fa4eaf153d01c4a7f401c2379215182bfda3493951d76a97a25ea370d450bf8d","observation_id":"1a10da74-6024-44bf-b362-2e88de0f4dee","resolution":{"observed_at":"2026-08-10T21:10:55.488207Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:10:55.462977Z","title":"Composition- based multi-relational graph convolutional networks,","venue":null,"work_id":"0caf4434-3fcc-4670-b7a9-bffd2bc1fa75","year":2019},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.251245Z"},"links":{"citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:e95e76b224895b1fda2abcd8e0a4d26f4ddd0a80adaa5eee6b4c6387dbbfabf9","observation_id":"c7ca4982-3c35-4f53-84bf-cff6bf49e49e","resolution":{"observed_at":"2026-08-10T21:10:55.468360Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:10:55.442299Z","title":"Distance encoding: Design provably more powerful neural networks for graph rep- resentation learning,","venue":null,"work_id":"58ddbebd-c0f4-48fb-89c0-da70e334b60a","year":2020},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.256755Z"},"links":{"citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:bea6e4f4f88b097f02b3adf61fbbd3bf1830530109d0462a075e7b24614be939","observation_id":"5a7c8928-7d82-4043-8745-075995c235c9","resolution":{"observed_at":"2026-08-10T21:10:55.449013Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:10:55.421730Z","title":"Learning knowledge graph embedding with heterogeneous relation atten- tion networks,","venue":null,"work_id":"1e7852a7-b713-4619-94f7-9b6e152ec45a","year":2021},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.263318Z"},"links":{"citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:aa6fc66f65064e8cd4be48676eaea04098e20c6ef31ddcb1264e5dd8277330ba","observation_id":"7ef3f066-1f61-4f05-9373-a2703d8488f8","resolution":{"observed_at":"2026-08-10T21:10:55.428408Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:10:55.401032Z","title":"Meta-knowledge transfer for inductive knowledge graph embed- ding,","venue":null,"work_id":"fc8478ad-5074-437d-8d93-f878a921e731","year":2022},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.268563Z"},"links":{"citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:d464455ba01b52c76536b807ba3cec18513551c8dfc439658aee39c96d2839d8","observation_id":"0afc20c1-8b1b-4590-a49b-ce9c3d9f7733","resolution":{"observed_at":"2026-08-10T21:10:55.408213Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:10:55.382197Z","title":"Indigo: Gnn-based inductive knowledge graph completion using pair-wise encod- ing,","venue":null,"work_id":"41f099f2-3f1e-4991-8fb8-942cce557ba6","year":2021},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.274061Z"},"links":{"citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:3573e51f0cba120f2940aba773331364a3c3047dde49120341a7603d59f20386","observation_id":"2a73295e-9479-4a31-b736-2026b0217e90","resolution":{"observed_at":"2026-08-10T21:10:55.387816Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:10:54.280162Z","title":"Inductive representation learning on large graphs,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.280162Z"},"links":{"citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:e3d0b31dbafe83bb3647b53526dd1ec0a37009300fc9b36d3c2a0fe3e3651df9","observation_id":"6ccfc10d-e45a-45ee-8921-b370c7232d68","resolution":{"observed_at":"2026-08-10T21:10:54.280162Z","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":"2026-08-10T21:10:55.355661Z","title":"Sim- plifying graph convolutional networks,","venue":null,"work_id":"458be414-2a87-492a-a9af-9839272187e7","year":2019},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.286329Z"},"links":{"citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:7583eaab9b14c3061293e506e0aeccf889f201ba5364511b509ad34b3966c185","observation_id":"2dcd706b-51a7-4af1-b94c-48ec3126053e","resolution":{"observed_at":"2026-08-10T21:10:55.360749Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:10:55.339516Z","title":"Gated graph sequence neural networks,","venue":null,"work_id":"2c9bd98e-90f7-48f2-929c-ca82a5822a04","year":2016},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.291467Z"},"links":{"citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:bbe64a4af9921f6c2f5531701c9345b2a353e5dfd82416fada3321c7476c2bb1","observation_id":"dabbe1fe-d192-4694-9215-c1b39a9a4415","resolution":{"observed_at":"2026-08-10T21:10:55.344831Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:10:55.321960Z","title":"A survey of heterogeneous information network analysis,","venue":null,"work_id":"37fe8faf-455a-4bf5-b59e-6973f3febbbf","year":2016},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.296520Z"},"links":{"citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:871942bba915089f7795614c6dbd79b51170b3f8a0f11bc98987484debaa6037","observation_id":"cc28270a-895e-470b-a2ca-c8d1c0129b6c","resolution":{"observed_at":"2026-08-10T21:10:55.327834Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:10:55.302597Z","title":"Mining heterogeneous information networks: principles and methodologies,","venue":null,"work_id":"8b12640d-163c-4df8-a840-b8dcc9a00d18","year":2012},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.302562Z"},"links":{"citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:1acae23b4e2b3dc6eeb008131132f367a59dac18240f28c57302c42e36b5448f","observation_id":"2d794a33-b131-4302-89d2-e708b0118931","resolution":{"observed_at":"2026-08-10T21:10:55.308206Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:10:55.282081Z","title":"Rankclus: integrating clustering with ranking for heterogeneous information network analysis,","venue":null,"work_id":"4488deeb-43df-4651-b81c-7d83f1bc6b16","year":2009},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.307940Z"},"links":{"citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:8607bbde10332ef6846d3b16e3256cfb89f8e7fac8b6bb1a12c7655b7b7aa837","observation_id":"e14a7b83-189b-4b7b-b29d-4a478e1c10d0","resolution":{"observed_at":"2026-08-10T21:10:55.288522Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:10:55.263011Z","title":"Collective prediction of multiple types of links in heterogeneous information networks,","venue":null,"work_id":"783f31f8-ab44-477a-a9bd-4a817e3383dc","year":2014},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.313710Z"},"links":{"citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:45c04f963cc6358bd704654a718579cea1da18acf52c08f0a50b94d63da42fd9","observation_id":"6f340e80-78d8-49f6-bc0b-dea6f7dcdb5a","resolution":{"observed_at":"2026-08-10T21:10:55.269008Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:10:55.241328Z","title":"A com- prehensive survey on graph neural networks,","venue":null,"work_id":"f3620448-f285-4dbe-b7f4-0ee4f8abea26","year":2021},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.320198Z"},"links":{"citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:cdadd0d3ac831f1493b3af9ae38861eee2a76d94b9210bed77761cd5281f2b2b","observation_id":"5842dd7d-77f9-4d24-9552-10d32f958a6a","resolution":{"observed_at":"2026-08-10T21:10:55.248535Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:10:55.217707Z","title":"Heterogeneous graph neural network,","venue":null,"work_id":"e794d637-7af7-4146-94c8-53f3197321bb","year":2019},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.325414Z"},"links":{"citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:177d20cb41ff1a5f62ebaf9a7e73348fd8db8450f1103ab9e4adf129a0122a29","observation_id":"29ab7d67-9a74-43b7-b927-054726c2f7c2","resolution":{"observed_at":"2026-08-10T21:10:55.222869Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:10:55.200358Z","title":"Non-recursive graph convo- lutional networks,","venue":null,"work_id":"d72ca71d-64b7-4ec3-9eae-fd1d9e0910de","year":2021},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.331200Z"},"links":{"citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:412fe55dcaba03d4396fdeebde07f4372368164575eef4aa661cd39dac853b9d","observation_id":"92cad412-115c-474d-82a0-ac4e1cb68065","resolution":{"observed_at":"2026-08-10T21:10:55.205649Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:10:55.182763Z","title":"Towards deeper graph neural net- works,","venue":null,"work_id":"64c44f85-6b46-4f4b-a6d6-ba0f7d13d914","year":2020},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.336855Z"},"links":{"citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:860a16347723f2b5ef52c181c53da6bece2e63c39ff0072dbb0caa5c1d286b8e","observation_id":"b875da5f-41bf-4fce-bcd4-0f4832bd3958","resolution":{"observed_at":"2026-08-10T21:10:55.188511Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:10:55.165607Z","title":"Modeling relational data with graph convolutional networks,","venue":null,"work_id":"d156e1ea-eeb3-4b62-94fe-45211ea0318c","year":2018},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.342673Z"},"links":{"citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:e889df9b600dcb2b983a11f1b82ac44dc3d92623216da739151494040fdc50ce","observation_id":"a1c4f85a-4499-4fe3-bc92-f3148f861f43","resolution":{"observed_at":"2026-08-10T21:10:55.170621Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:10:55.146894Z","title":"Heterogeneous graph attention network,","venue":null,"work_id":"1d2dfb60-c6ce-4eeb-b34e-11ce13ccd2db","year":2019},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.347633Z"},"links":{"citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:10308d2dbc4d2241f3ae7ac345affa6fe1f40284ac100f1fa5b25cd08484d37d","observation_id":"e6971690-a173-45c1-9902-ad9e7d891c74","resolution":{"observed_at":"2026-08-10T21:10:55.152576Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:10:55.128084Z","title":"Magnn: Metapath ag- gregated graph neural network for heterogeneous graph embed- ding,","venue":null,"work_id":"ce447e7b-0107-4fe9-81ab-e1fa3adea001","year":2020},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.352543Z"},"links":{"citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:89c9ae0dbf6916482be22620367be1dc8d57f554ca54d2b9676bb0ea69176fcd","observation_id":"ad15dcf1-bf8d-46c5-8b71-51cb905a8a07","resolution":{"observed_at":"2026-08-10T21:10:55.134371Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:10:55.103217Z","title":"Simple and efficient heterogeneous graph neural network,","venue":null,"work_id":"91021713-f19f-4bb8-bd6a-15876b806d17","year":2023},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.357159Z"},"links":{"citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:e770cd24d84163ffe5a109fb2250b1bcc2f8ec50f4836401cdd3135e9b07345f","observation_id":"dbbfb431-4305-4047-86df-8bc704a8a5ed","resolution":{"observed_at":"2026-08-10T21:10:55.110762Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:10:55.075191Z","title":"Pathsim: Meta path-based top-k similarity search in heterogeneous information networks,","venue":null,"work_id":"9404975e-3939-47fa-906e-ceb378515c78","year":2011},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.362862Z"},"links":{"citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:3025b014afec0a02f2ccc17679ced92e647ba3ce38d76e3d39070d53f169c493","observation_id":"4d1ba4b6-2af6-434c-ac4b-cc3cb206feb9","resolution":{"observed_at":"2026-08-10T21:10:55.084342Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:10:54.367704Z","title":"Discovering meta-paths in large heterogeneous information networks,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.367704Z"},"links":{"citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:7eda1e3c1d59cf0e7c6138a493678f7ea4a49501c55886659061021e40141e20","observation_id":"ab241ffb-9f54-4221-902e-e3634009d02f","resolution":{"observed_at":"2026-08-10T21:10:54.367704Z","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":"2026-08-10T21:10:55.057840Z","title":"Higher- order attribute-enhancing heterogeneous graph neural networks,","venue":null,"work_id":"2aa7e743-cc5c-4daf-94fd-e064737bec58","year":2021},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.372286Z"},"links":{"citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:eda6513ac00ca86a49083f4d17de75fd4c961f3d0404cee473049d41557c123d","observation_id":"e44a037a-bf15-49c6-b24d-43131ea5b5c8","resolution":{"observed_at":"2026-08-10T21:10:55.063008Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:10:55.040164Z","title":"Graph trans- former networks,","venue":null,"work_id":"0c31040b-62af-4d22-a714-a6205c806561","year":2019},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.376872Z"},"links":{"citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:358a611f6ee10b9b2da8503100ca91491112ec1ce42c51223a6789609e99251f","observation_id":"5ccb4b82-498b-49b3-b57f-df1b89c77e74","resolution":{"observed_at":"2026-08-10T21:10:55.046069Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:10:55.020953Z","title":"Heterogeneous graph transformer,","venue":null,"work_id":"4adbf1f1-54a8-4c1a-b65b-4f0c90afa225","year":2020},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.381385Z"},"links":{"citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:64dd3aa91aa4990b815d6622194ef5be46b849fa00d1bec8ff1acf779c684d34","observation_id":"c6c66b2d-65f0-41cc-ab95-bddee79fbcf2","resolution":{"observed_at":"2026-08-10T21:10:55.027644Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:10:54.996884Z","title":"Mhnf: Multi-hop hetero- geneous neighborhood information fusion graph representation learning,","venue":null,"work_id":"b4a5d6af-715c-4957-b61b-cc607d3d2891","year":2022},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.386150Z"},"links":{"citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:b61babf3c5ef31bf4662e791479c723b8ded7df8085a15092b6c52a37328f9d5","observation_id":"33617ebb-8edf-49e6-848c-dd0afa7fdac6","resolution":{"observed_at":"2026-08-10T21:10:55.003909Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:10:54.978342Z","title":"Are we really making much progress? revisiting, benchmarking and refining heterogeneous graph neural networks,","venue":null,"work_id":"e8fdb9b0-c90a-4360-99da-a6d8fd32c3b3","year":2021},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.391085Z"},"links":{"citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:8b4339580d840c719f41be86eb0fbb7191af187449b97169c84ed797831f8134","observation_id":"24c31382-9569-46b2-8e12-5dd99a80e662","resolution":{"observed_at":"2026-08-10T21:10:54.984656Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:10:54.958518Z","title":"Efficient heterogeneous graph learning via random projection,","venue":null,"work_id":"56c3b99b-9d49-407f-af81-62c353e9032e","year":2024},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.395785Z"},"links":{"citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:e0e600149055643dbe2f0de35f5f347978f1cc433c6cdb4d1e47901ba9265580","observation_id":"08bbc367-8c42-4245-ad82-7ea9ef80b0ba","resolution":{"observed_at":"2026-08-10T21:10:54.965548Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:10:54.939462Z","title":"Graph neural networks with a distribution of parametrized graphs,","venue":null,"work_id":"e673ea7c-1d55-4cc9-8e99-13324147ef3d","year":2024},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.400325Z"},"links":{"citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:2086b0f9ffe68652892e3c76cdeb666f71df74728b2edbe90c504662dcf6b387","observation_id":"6da7b457-14a2-4b04-93fa-ddff7563bd9d","resolution":{"observed_at":"2026-08-10T21:10:54.945423Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:10:54.915592Z","title":"Graph neural architecture search,","venue":null,"work_id":"a6fabd03-7c5e-4c18-87e9-60316db59218","year":2021},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.404949Z"},"links":{"citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:d7934cd19055515d55090f40997a4ce084972b1df4d64f6ac79f6381598df205","observation_id":"4da32df4-d6ec-45ce-ad1f-09722ebb3348","resolution":{"observed_at":"2026-08-10T21:10:54.922087Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1909.03184","last_updated":"2019-09-10T01:14:33Z","snapshot_observed_at":"2026-08-13T18:11:43.604377Z","submitted_at":"2019-09-07T04:10:41Z","title":"Auto-GNN: Neural Architecture Search of Graph Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.03184","snapshot_observed_at":"2026-08-10T21:10:54.410324Z","title":"Auto-gnn: Neural architecture search of graph neural networks,","venue":null,"work_id":null,"year":1909},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.410324Z"},"links":{"cited_paper":"/paper/1909.03184","citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:1eae1e0c001fafc544f3b39cb9a45f83b43273e7c7d687bafb2615919ed960fa","observation_id":"b32b9f0a-2aaf-46dd-a9c6-303952fcd5c4","resolution":{"observed_at":"2026-08-10T21:10:54.410324Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.08582","last_updated":"2022-06-17T06:47:21Z","snapshot_observed_at":"2026-08-17T16:58:21.948493Z","submitted_at":"2022-06-17T06:47:21Z","title":"DFG-NAS: Deep and Flexible Graph Neural Architecture Search","version":1},"cited_work":{"arxiv_id":"2206.08582","doi":null,"metadata_source":"pith","pith_arxiv_id":"2206.08582","snapshot_observed_at":"2026-08-10T21:10:54.612151Z","title":"DFG-NAS: Deep and Flexible Graph Neural Architecture Search","venue":"cs.LG","work_id":"e3b6a944-5f4d-47e9-b25b-1eedf1b48c68","year":2022},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.420191Z"},"links":{"cited_paper":"/paper/2206.08582","citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:e570fc7d054fbc23f41ab41b0ebfd9fa3e5be38b009087f3325825983d9eb120","observation_id":"66d09ed5-aa1a-4784-b6ab-2d2afb3470ab","resolution":{"observed_at":"2026-08-10T21:10:54.619930Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:10:54.896856Z","title":"Large-scale graph neural architecture search,","venue":null,"work_id":"111e5f49-74ac-411c-a19a-ca7e272c592d","year":2022},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.426476Z"},"links":{"citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:749e73406bd9ebf40732544e9a6f00ee4c2fb27fe2b77de96a1516fdb198a3c4","observation_id":"966decab-32ec-4747-9cd1-b1625a07efbe","resolution":{"observed_at":"2026-08-10T21:10:54.903021Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:10:54.874383Z","title":"Genetic meta-structure search for recommendation on heterogeneous in- formation network,","venue":null,"work_id":"9d4c7a0c-1b28-4948-acf6-22ac439afb4b","year":2020},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.432329Z"},"links":{"citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:312b8781b7eafc6b1818a188e5f9af92a6902c2f4cb208d936f1b19ce5f2e4e3","observation_id":"8e819694-372d-4a33-b00b-c5d3a4368ad6","resolution":{"observed_at":"2026-08-10T21:10:54.882635Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:10:54.854012Z","title":"Autogel: An automated graph neural network with explicit link information,","venue":null,"work_id":"0c55328a-e580-4c6b-a1c1-9baee64e6ff6","year":2021},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.437571Z"},"links":{"citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:7568320cbd42aa8eb53e9e3a7fe67b128d340b004f4cada26a54a24395f84c32","observation_id":"cc397082-34ce-416d-9954-0cceac03229d","resolution":{"observed_at":"2026-08-10T21:10:54.860100Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:10:54.831994Z","title":"Diffmg: Differentiable meta graph search for heterogeneous graph neural networks,","venue":null,"work_id":"198f59c3-d618-4ccd-a32e-dcd2ead8adfb","year":2021},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.442323Z"},"links":{"citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:71742ba9895f7b5c818d446acffd0d0257df0ecf83b2b6e096614b50bba61a9a","observation_id":"651f5dfa-f07d-4a49-a687-e7c3646ecb7d","resolution":{"observed_at":"2026-08-10T21:10:54.839209Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:10:54.810165Z","title":"Measuring and relieving the over-smoothing problem for graph neural networks from the topological view,","venue":null,"work_id":"73aa5650-4e7d-4836-a1b1-c8894cd24816","year":2019},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.446871Z"},"links":{"citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:d1c7a1119ed9c32c8b75ac6d0e62253413d1931800fcb7777e2efb739fad972a","observation_id":"e3d5c72e-aee2-4ae0-b3eb-a98f180c936b","resolution":{"observed_at":"2026-08-10T21:10:54.816717Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:10:54.790512Z","title":"Hierarchical optimization: An introduction,","venue":null,"work_id":"e5ef324e-5ace-43e0-bd74-fe685f296a21","year":1992},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.451780Z"},"links":{"citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:a9edefb10b2361e742855030ef87de527c9e4fc902439175efb20b8e45f92c83","observation_id":"dec963b7-737f-4afb-b290-88decbfa77c0","resolution":{"observed_at":"2026-08-10T21:10:54.796324Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:10:54.770821Z","title":"An overview of bilevel optimization,","venue":null,"work_id":"2db21169-bc77-4323-a280-4e000aa31e22","year":2007},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.456311Z"},"links":{"citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:98b9e047dfd9dc070d96204ea72bc6f2520e9d970d03cb855b466e617b9706b2","observation_id":"e38ed35c-f99b-4138-b265-90cb76381c0a","resolution":{"observed_at":"2026-08-10T21:10:54.776508Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.09055","last_updated":"2019-04-23T06:29:32Z","snapshot_observed_at":"2026-08-15T06:40:14.261008Z","submitted_at":"2018-06-24T00:06:13Z","title":"DARTS: Differentiable Architecture Search","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1806.09055","snapshot_observed_at":"2026-08-10T21:10:54.462485Z","title":"Darts: Differentiable architec- ture search,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.462485Z"},"links":{"cited_paper":"/paper/1806.09055","citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:b6404733b24b7de5d1e972b0d988267f03870825fbe7ad9ece062dea414ee484","observation_id":"aeeb2046-9ab3-4cff-b200-008310ba0139","resolution":{"observed_at":"2026-08-10T21:10:54.462485Z","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":"2026-08-10T21:10:54.751834Z","title":"Graph attention networks,","venue":null,"work_id":"f6a50aee-8d18-40d9-b800-bc465a30f3ec","year":2017},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.468373Z"},"links":{"citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:f5be31287312c784ec0d36a4b2fd236156f066606d2543b3cb4e255fde3e12f1","observation_id":"924cba55-0ce1-4b78-a5f4-2ef5e4aedaa9","resolution":{"observed_at":"2026-08-10T21:10:54.757424Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:10:54.730228Z","title":"Semi-supervised classification with graph convolutional networks,","venue":null,"work_id":"23755428-73d4-4445-b356-023098ddb19e","year":2016},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.474037Z"},"links":{"citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:8370691b6c1794a8b2cc028299ba340f6d5789e4a26f18031b958319de1fd2c1","observation_id":"43a1c0b7-cf19-4047-bfc3-e95ae0e1e8d8","resolution":{"observed_at":"2026-08-10T21:10:54.736968Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:10:54.478962Z","title":"Heterogeneous network representation learning: A unified framework with survey and benchmark,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-10T21:10:54.478962Z"},"links":{"citing_paper":"/paper/2501.07598"},"observation_digest":"sha256:06778b781ead6f230d1e1031f6b9a418f03773870a9ad0d5085c0981149d0012","observation_id":"fc440fc4-1560-4e2f-ace9-661893be8388","resolution":{"observed_at":"2026-08-10T21:10:54.478962Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2501.07598","last_updated":"2025-01-10T14:26:10Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-10T21:03:54.797137Z","submitted_at":"2025-01-10T14:26:10Z","title":"Automated Heterogeneous Network learning with Non-Recursive Message Passing"},"reference_resolution":{"displayed":47,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":5,"verified_exact":1,"verified_fuzzy":41},"total_outbound_references":47},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2501.07598."}