{"as_of":"2026-08-23T19:24:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2e16560f41a5da01df0e311719125e6e9a666ea72ec00a5a6af91b9b64e2b26c","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":12,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":12,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-23T06:30:58.430688+00:00","state":"measured"},{"denominator":12,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":12,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T04:51:32.832958Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-07-11T01:47:47.693612Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1803.07294","last_updated":"2018-03-20T08:33:20Z","snapshot_observed_at":"2026-08-14T19:34:30.155165Z","submitted_at":"2018-03-20T08:33:20Z","title":"GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs","version":1},"cited_work":{"arxiv_id":"1803.07294","doi":null,"metadata_source":"pith","pith_arxiv_id":"1803.07294","snapshot_observed_at":"2026-07-11T01:47:47.693612Z","title":"GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs","venue":"cs.LG","work_id":"10d81639-1b59-491e-80fd-dcf342e3f6fb","year":2018},"citing_paper":{"arxiv_id":"1906.12330","last_updated":"2019-06-21T03:05:17Z","snapshot_observed_at":"2026-08-20T12:23:54.429855Z","submitted_at":"2019-06-21T03:05:17Z","title":"Graph Star Net for Generalized Multi-Task Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-25T18:54:30.043952Z"},"links":{"cited_paper":"/paper/1803.07294","citing_paper":"/paper/1906.12330"},"observation_digest":"sha256:6fa8e33a7629455e285a4539037b79890ac53abc009baf1c181d345dc143728b","observation_id":"43cca05d-b337-4e6b-aac3-11cbc4c5d8e9","resolution":{"observed_at":"2026-05-25T18:56:08.870293Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1803.07294","last_updated":"2018-03-20T08:33:20Z","snapshot_observed_at":"2026-08-14T19:34:30.155165Z","submitted_at":"2018-03-20T08:33:20Z","title":"GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.07294","snapshot_observed_at":"2026-08-14T12:19:05.759658Z","title":"arXiv preprint arXiv:1803.07294 (2018)","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.07558","last_updated":"2020-02-26T17:00:28Z","snapshot_observed_at":"2026-08-18T19:14:32.057186Z","submitted_at":"2019-08-20T18:24:32Z","title":"Transferring Robustness for Graph Neural Network Against Poisoning Attacks","version":3},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-14T12:19:05.759658Z"},"links":{"cited_paper":"/paper/1803.07294","citing_paper":"/paper/1908.07558"},"observation_digest":"sha256:3d4740f9705d6549aa00a7666dd210f396e7b1bde2794998841b348b9168702b","observation_id":"2314465a-5255-4a45-8a5a-4ec74c4a3a21","resolution":{"observed_at":"2026-08-14T12:19:05.759658Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1803.07294","last_updated":"2018-03-20T08:33:20Z","snapshot_observed_at":"2026-08-14T19:34:30.155165Z","submitted_at":"2018-03-20T08:33:20Z","title":"GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs","version":1},"cited_work":{"arxiv_id":"1803.07294","doi":null,"metadata_source":"pith","pith_arxiv_id":"1803.07294","snapshot_observed_at":"2026-07-11T01:47:47.693612Z","title":"GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs","venue":"cs.LG","work_id":"10d81639-1b59-491e-80fd-dcf342e3f6fb","year":2018},"citing_paper":{"arxiv_id":"2104.13478","last_updated":"2021-05-02T16:16:03Z","snapshot_observed_at":"2026-08-12T23:06:05.148534Z","submitted_at":"2021-04-27T21:09:51Z","title":"Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges","version":2},"reference_index":104,"source":"pdf_text","source_observed_at":"2026-05-13T02:39:29.411021Z"},"links":{"cited_paper":"/paper/1803.07294","citing_paper":"/paper/2104.13478"},"observation_digest":"sha256:99ffedc0de00761c0f97f469ad88f8f73c2db9852cf4503b175411d5bd8864b0","observation_id":"5bffce14-66e6-4d7a-a1aa-b741834c4c14","resolution":{"observed_at":"2026-05-13T02:39:30.051394Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1803.07294","last_updated":"2018-03-20T08:33:20Z","snapshot_observed_at":"2026-08-14T19:34:30.155165Z","submitted_at":"2018-03-20T08:33:20Z","title":"GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.07294","snapshot_observed_at":"2026-08-12T21:35:10.958038Z","title":"Gaan: 26 Gatedattentionnetworksforlearningonlargeandspatiotemporalgraphs","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.08550","last_updated":"2025-06-22T06:12:15Z","snapshot_observed_at":"2026-08-16T04:05:18.324961Z","submitted_at":"2024-11-13T11:59:40Z","title":"Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T21:35:10.958038Z"},"links":{"cited_paper":"/paper/1803.07294","citing_paper":"/paper/2411.08550"},"observation_digest":"sha256:4a70277859f41c0897c69108457905b1fbdd38dd915e852763744598317b439d","observation_id":"f55659fe-f359-4a7d-baf2-4223b1886a14","resolution":{"observed_at":"2026-08-12T21:35:10.958038Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1803.07294","last_updated":"2018-03-20T08:33:20Z","snapshot_observed_at":"2026-08-14T19:34:30.155165Z","submitted_at":"2018-03-20T08:33:20Z","title":"GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.07294","snapshot_observed_at":"2026-08-10T14:28:16.995842Z","title":"Gaan: Gated attention net- works for learning on large and spatiotemporal graphs","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.15348","last_updated":"2025-01-25T23:16:03Z","snapshot_observed_at":"2026-08-15T19:53:34.575722Z","submitted_at":"2025-01-25T23:16:03Z","title":"ReInc: Scaling Training of Dynamic Graph Neural Networks","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-10T14:28:16.995842Z"},"links":{"cited_paper":"/paper/1803.07294","citing_paper":"/paper/2501.15348"},"observation_digest":"sha256:c92af24bd770eca94798d92c8c97c5c9d5c3bca34a2c9574ae67d08547615ee8","observation_id":"317ce46e-a84c-4f8e-b1a8-2641ff57a484","resolution":{"observed_at":"2026-08-10T14:28:16.995842Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1803.07294","last_updated":"2018-03-20T08:33:20Z","snapshot_observed_at":"2026-08-14T19:34:30.155165Z","submitted_at":"2018-03-20T08:33:20Z","title":"GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.07294","snapshot_observed_at":"2026-08-16T04:51:32.832958Z","title":"arXiv preprint arXiv:1803.07294 (2018)","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.00302","last_updated":"2025-05-01T04:50:00Z","snapshot_observed_at":"2026-08-19T19:24:15.645671Z","submitted_at":"2025-05-01T04:50:00Z","title":"Temporal Attention Evolutional Graph Convolutional Network for Multivariate Time Series Forecasting","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-16T04:51:32.832958Z"},"links":{"cited_paper":"/paper/1803.07294","citing_paper":"/paper/2505.00302"},"observation_digest":"sha256:4138b6b64e398fa51d5635d8fc6a430bf425ddc33149ac5d128d59fa2fe61831","observation_id":"0b430c9e-ba6d-4252-b903-e5dd89be6c57","resolution":{"observed_at":"2026-08-16T04:51:32.832958Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1803.07294","last_updated":"2018-03-20T08:33:20Z","snapshot_observed_at":"2026-08-14T19:34:30.155165Z","submitted_at":"2018-03-20T08:33:20Z","title":"GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.07294","snapshot_observed_at":"2026-08-06T10:23:13.088700Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.00141","last_updated":"2025-07-31T20:00:35Z","snapshot_observed_at":"2026-08-18T16:39:14.083390Z","submitted_at":"2025-07-31T20:00:35Z","title":"INSPIRE-GNN: Intelligent Sensor Placement to Improve Sparse Bicycling Network Prediction via Reinforcement Learning Boosted Graph Neural Networks","version":1},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-06T10:23:13.088700Z"},"links":{"cited_paper":"/paper/1803.07294","citing_paper":"/paper/2508.00141"},"observation_digest":"sha256:b9b7100dd41c17101753abe98702412252c1c9159eda71499207903f116892b7","observation_id":"13c7d1c3-57f8-44e5-992b-d114bb1a8cf7","resolution":{"observed_at":"2026-08-06T10:23:13.088700Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1803.07294","last_updated":"2018-03-20T08:33:20Z","snapshot_observed_at":"2026-08-14T19:34:30.155165Z","submitted_at":"2018-03-20T08:33:20Z","title":"GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs","version":1},"cited_work":{"arxiv_id":"1803.07294","doi":null,"metadata_source":"pith","pith_arxiv_id":"1803.07294","snapshot_observed_at":"2026-07-11T01:47:47.693612Z","title":"GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs","venue":"cs.LG","work_id":"10d81639-1b59-491e-80fd-dcf342e3f6fb","year":2018},"citing_paper":{"arxiv_id":"2605.16726","last_updated":"2026-05-16T00:28:59Z","snapshot_observed_at":"2026-08-16T13:54:17.776941Z","submitted_at":"2026-05-16T00:28:59Z","title":"A Global-Local Graph Attention Network for Traffic Forecasting","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-19T21:42:47.975794Z"},"links":{"cited_paper":"/paper/1803.07294","citing_paper":"/paper/2605.16726"},"observation_digest":"sha256:0b84af9f42b4380374a23f08235411f3c3be8c7fe6039327ad4782628e06fc7b","observation_id":"0744b6da-b711-4cec-8d08-342c33f6b4b8","resolution":{"observed_at":"2026-05-19T21:43:13.565126Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1803.07294","last_updated":"2018-03-20T08:33:20Z","snapshot_observed_at":"2026-08-14T19:34:30.155165Z","submitted_at":"2018-03-20T08:33:20Z","title":"GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs","version":1},"cited_work":{"arxiv_id":"1803.07294","doi":null,"metadata_source":"pith","pith_arxiv_id":"1803.07294","snapshot_observed_at":"2026-07-11T01:47:47.693612Z","title":"GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs","venue":"cs.LG","work_id":"10d81639-1b59-491e-80fd-dcf342e3f6fb","year":2018},"citing_paper":{"arxiv_id":"2605.23183","last_updated":"2026-05-22T03:05:34Z","snapshot_observed_at":"2026-08-19T14:35:05.804233Z","submitted_at":"2026-05-22T03:05:34Z","title":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-25T03:25:44.104161Z"},"links":{"cited_paper":"/paper/1803.07294","citing_paper":"/paper/2605.23183"},"observation_digest":"sha256:009c5d3d71315f570c0817cacd2b25bad5ea80fcd9b1cacff0bc7a27970bf82c","observation_id":"3180a4fc-c121-4701-8fbb-81092a451f93","resolution":{"observed_at":"2026-05-25T03:26:35.633233Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1803.07294","last_updated":"2018-03-20T08:33:20Z","snapshot_observed_at":"2026-08-14T19:34:30.155165Z","submitted_at":"2018-03-20T08:33:20Z","title":"GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs","version":1},"cited_work":{"arxiv_id":"1803.07294","doi":null,"metadata_source":"pith","pith_arxiv_id":"1803.07294","snapshot_observed_at":"2026-07-11T01:47:47.693612Z","title":"GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs","venue":"cs.LG","work_id":"10d81639-1b59-491e-80fd-dcf342e3f6fb","year":2018},"citing_paper":{"arxiv_id":"2606.09432","last_updated":"2026-06-08T12:42:10Z","snapshot_observed_at":"2026-07-06T23:48:48.962930Z","submitted_at":"2026-06-08T12:42:10Z","title":"Graph Mamba Operator: A Latent Simulator for Interacting Particle Systems","version":1},"reference_index":120,"source":"arxiv_source","source_observed_at":"2026-06-27T17:07:27.417845Z"},"links":{"cited_paper":"/paper/1803.07294","citing_paper":"/paper/2606.09432"},"observation_digest":"sha256:bcf9427a3224617e646bb0556425002cbeb72dae137ac18809103b00908ad18e","observation_id":"7e98ef59-0680-4023-8abf-b639b360b840","resolution":{"observed_at":"2026-07-03T00:37:29.955134Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1803.07294","last_updated":"2018-03-20T08:33:20Z","snapshot_observed_at":"2026-08-14T19:34:30.155165Z","submitted_at":"2018-03-20T08:33:20Z","title":"GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs","version":1},"cited_work":{"arxiv_id":"1803.07294","doi":null,"metadata_source":"pith","pith_arxiv_id":"1803.07294","snapshot_observed_at":"2026-07-11T01:47:47.693612Z","title":"GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs","venue":"cs.LG","work_id":"10d81639-1b59-491e-80fd-dcf342e3f6fb","year":2018},"citing_paper":{"arxiv_id":"2607.06614","last_updated":"2026-07-07T08:15:20Z","snapshot_observed_at":"2026-08-21T13:22:56.318582Z","submitted_at":"2026-07-07T08:15:20Z","title":"STAGformer: A Spatio-temporal Agent Graph Transformer for Micro Mobility Demand Forecasting","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-07-11T01:41:20.147919Z"},"links":{"cited_paper":"/paper/1803.07294","citing_paper":"/paper/2607.06614"},"observation_digest":"sha256:4caec738abcf2abbd5ea41608f3e089ef19f8759c181e11d4e50298ee3a39ab9","observation_id":"870ff8ee-0aaf-46b3-ae05-fbf9b3f3ac20","resolution":{"observed_at":"2026-07-11T01:47:47.716206Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1803.07294","last_updated":"2018-03-20T08:33:20Z","snapshot_observed_at":"2026-08-14T19:34:30.155165Z","submitted_at":"2018-03-20T08:33:20Z","title":"GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.07294","snapshot_observed_at":"2026-08-02T05:50:44.102338Z","title":"arXiv preprint arXiv:1803.07294 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.13241","last_updated":"2026-07-14T20:02:42Z","snapshot_observed_at":"2026-08-12T15:18:25.821761Z","submitted_at":"2026-07-14T20:02:42Z","title":"EMAGN: Efficient Multi-Attention Graph Network via Learned Clustering for Scalable Traffic Forecasting","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-02T05:50:44.102338Z"},"links":{"cited_paper":"/paper/1803.07294","citing_paper":"/paper/2607.13241"},"observation_digest":"sha256:65f4382c4251b00a9a63ec522bc668af3bb81066e65ec9fa11523e4f76516ee7","observation_id":"16e122eb-1204-4888-8094-10d94075c3ab","resolution":{"observed_at":"2026-08-02T05:50:44.102338Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1803.07294/citation-record","integrity":"/paper/1803.07294/integrity","json":"/paper/1803.07294/citation-record.json","paper":"/paper/1803.07294"},"outbound":[],"paper":{"arxiv_id":"1803.07294","last_updated":"2018-03-20T08:33:20Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-14T19:34:30.155165Z","submitted_at":"2018-03-20T08:33:20Z","title":"GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs"},"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":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"thesis":"As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:1803.07294."}