{"as_of":"2026-08-13T10:34:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e746cb8763a42b031ea3a9b1fb144207f5753932f95550ca53d60f62081ad869","coverage":[{"denominator":13,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":13,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T19:12:29.020671Z","state":"measured"},{"denominator":13,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":13,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+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/2411.10957/citation-record","integrity":"/paper/2411.10957/integrity","json":"/paper/2411.10957/citation-record.json","paper":"/paper/2411.10957"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:12:29.166635Z","title":"Data for t = 2018 and t = 2019 were excluded, and no scaling corrections were applied","venue":null,"work_id":"196eebef-ba57-4514-8010-be2ee418ba7d","year":2018},"citing_paper":{"arxiv_id":"2411.10957","last_updated":"2024-11-17T04:23:25Z","snapshot_observed_at":"2026-08-13T09:21:09.961803Z","submitted_at":"2024-11-17T04:23:25Z","title":"IMPaCT GNN: Imposing invariance with Message Passing in Chronological split Temporal Graphs","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T19:12:29.002464Z"},"links":{"citing_paper":"/paper/2411.10957"},"observation_digest":"sha256:a0f67692dcfc3b09cdbadf9e7259c15ac14fbf6ba28ab4dd86b8c8e8e1ef0f2c","observation_id":"fb4a98ac-e048-4d14-abf2-1fef663c7db9","resolution":{"observed_at":"2026-08-12T19:12:29.172115Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T19:12:29.153847Z","title":"A.2 T OY EXPERIMENT The purpose of toy experiment was to compare test accuracy obtained when dataset was split chrono- logically and split randomly regardless of time information","venue":null,"work_id":"3054bcff-a541-4f62-97f5-d6bee7d3bb0c","year":2020},"citing_paper":{"arxiv_id":"2411.10957","last_updated":"2024-11-17T04:23:25Z","snapshot_observed_at":"2026-08-13T09:21:09.961803Z","submitted_at":"2024-11-17T04:23:25Z","title":"IMPaCT GNN: Imposing invariance with Message Passing in Chronological split Temporal Graphs","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T19:12:29.006799Z"},"links":{"citing_paper":"/paper/2411.10957"},"observation_digest":"sha256:67ea675404b22a8ed92bcfde582d674917955cfc081c96b7108dbc0102d9ff48","observation_id":"f31e0727-0eb0-49fb-b6e5-4170abeaf1b5","resolution":{"observed_at":"2026-08-12T19:12:29.158066Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2004.11198","last_updated":"2020-11-03T19:20:22Z","snapshot_observed_at":"2026-08-13T09:20:38.675527Z","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":"2026-08-12T19:12:28.984329Z","title":"Sign: Scalable inception graph neural networks","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2411.10957","last_updated":"2024-11-17T04:23:25Z","snapshot_observed_at":"2026-08-13T09:21:09.961803Z","submitted_at":"2024-11-17T04:23:25Z","title":"IMPaCT GNN: Imposing invariance with Message Passing in Chronological split Temporal Graphs","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T19:12:28.984329Z"},"links":{"cited_paper":"/paper/2004.11198","citing_paper":"/paper/2411.10957"},"observation_digest":"sha256:6a03eb268327c4a58bb16359570399fef0e80b03c627e2831726a6a6dc2a5d4e","observation_id":"cb904834-6b1e-4110-9038-427afe6699c1","resolution":{"observed_at":"2026-08-12T19:12:28.984329Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.06522","last_updated":"2024-05-10T15:06:53Z","snapshot_observed_at":"2026-08-13T09:21:40.827169Z","submitted_at":"2024-05-10T15:06:53Z","title":"Heterogeneous Graph Neural Networks with Loss-decrease-aware Curriculum Learning","version":1},"cited_work":{"arxiv_id":"2405.06522","doi":null,"metadata_source":"pith","pith_arxiv_id":"2405.06522","snapshot_observed_at":"2026-08-12T19:12:29.062022Z","title":"Heterogeneous Graph Neural Networks with Loss-decrease-aware Curriculum Learning","venue":"cs.LG","work_id":"adf95114-3239-40a9-9929-ca34e4017213","year":2024},"citing_paper":{"arxiv_id":"2411.10957","last_updated":"2024-11-17T04:23:25Z","snapshot_observed_at":"2026-08-13T09:21:09.961803Z","submitted_at":"2024-11-17T04:23:25Z","title":"IMPaCT GNN: Imposing invariance with Message Passing in Chronological split Temporal Graphs","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T19:12:28.988795Z"},"links":{"cited_paper":"/paper/2405.06522","citing_paper":"/paper/2411.10957"},"observation_digest":"sha256:266754b3685388a6838329a9d819fa60797bbded1bb05b2bea17241e104de411","observation_id":"338cf7cf-d674-4ec3-8daf-180a019e5a45","resolution":{"observed_at":"2026-08-12T19:12:29.068772Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T19:12:29.180982Z","title":"The figure on the right considers only the 15 labels with the most nodes, redrawing the graph for clarity","venue":null,"work_id":"4dffee69-49e3-410a-988e-c1a5198f4bed","year":2018},"citing_paper":{"arxiv_id":"2411.10957","last_updated":"2024-11-17T04:23:25Z","snapshot_observed_at":"2026-08-13T09:21:09.961803Z","submitted_at":"2024-11-17T04:23:25Z","title":"IMPaCT GNN: Imposing invariance with Message Passing in Chronological split Temporal Graphs","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T19:12:28.998167Z"},"links":{"citing_paper":"/paper/2411.10957"},"observation_digest":"sha256:a9bccff466c95f19e48c42f2a4cfed7e8dfa1cee1be2a361d0b11a9b788215e7","observation_id":"1cab827f-99ed-4d6f-92d2-005c580582f0","resolution":{"observed_at":"2026-08-12T19:12:29.185377Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T19:12:29.139855Z","title":"The performance metric is accuracy, representing the proportion of correctly labeled nodes among all test nodes","venue":null,"work_id":"a66413cb-0bd3-45ad-b969-82018af3d34e","year":2019},"citing_paper":{"arxiv_id":"2411.10957","last_updated":"2024-11-17T04:23:25Z","snapshot_observed_at":"2026-08-13T09:21:09.961803Z","submitted_at":"2024-11-17T04:23:25Z","title":"IMPaCT GNN: Imposing invariance with Message Passing in Chronological split Temporal Graphs","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T19:12:29.010638Z"},"links":{"citing_paper":"/paper/2411.10957"},"observation_digest":"sha256:03fb624fa4e9bc15167d4f83624e6c94f8aefe05fe9fa26466e58f5a145172f8","observation_id":"a1e727f6-4495-403d-a3e8-55aa55470db8","resolution":{"observed_at":"2026-08-12T19:12:29.144862Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T19:12:29.112186Z","title":null,"venue":null,"work_id":"9f157b7a-b6cc-4b3a-bce3-8746cdd3f720","year":2000},"citing_paper":{"arxiv_id":"2411.10957","last_updated":"2024-11-17T04:23:25Z","snapshot_observed_at":"2026-08-13T09:21:09.961803Z","submitted_at":"2024-11-17T04:23:25Z","title":"IMPaCT GNN: Imposing invariance with Message Passing in Chronological split Temporal Graphs","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T19:12:29.020671Z"},"links":{"citing_paper":"/paper/2411.10957"},"observation_digest":"sha256:93605ba410363e8e8f8396a7683a1253183ded720c8972af88fa942d4f541c0a","observation_id":"4f9f1fa1-152e-480c-bdd2-680867842431","resolution":{"observed_at":"2026-08-12T19:12:29.116861Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.14481","last_updated":"2024-09-03T07:46:24Z","snapshot_observed_at":"2026-08-13T09:51:24.515117Z","submitted_at":"2023-10-23T01:25:44Z","title":"Efficient Heterogeneous Graph Learning via Random Projection","version":2},"cited_work":{"arxiv_id":"2310.14481","doi":null,"metadata_source":"pith","pith_arxiv_id":"2310.14481","snapshot_observed_at":"2026-08-12T19:12:29.097782Z","title":"Efficient Heterogeneous Graph Learning via Random Projection","venue":"cs.LG","work_id":"858e2ae8-9683-4d2d-a4c8-b28762fe05a6","year":2023},"citing_paper":{"arxiv_id":"2411.10957","last_updated":"2024-11-17T04:23:25Z","snapshot_observed_at":"2026-08-13T09:21:09.961803Z","submitted_at":"2024-11-17T04:23:25Z","title":"IMPaCT GNN: Imposing invariance with Message Passing in Chronological split Temporal Graphs","version":1},"reference_index":1983,"source":"pdf_text","source_observed_at":"2026-08-12T19:12:28.975828Z"},"links":{"cited_paper":"/paper/2310.14481","citing_paper":"/paper/2411.10957"},"observation_digest":"sha256:1036049209e8d8a9b4fdf62e8e2e43696ef7f700c4ceb3e6e7c7b5d8cb8a9946","observation_id":"df45d156-6b4a-48d2-adcf-f4137f3ae661","resolution":{"observed_at":"2026-08-12T19:12:29.102808Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T19:12:29.206592Z","title":"Node feature extraction by self-supervised multi-scale neighborhood predic- tion","venue":null,"work_id":"c93dafd5-9640-4561-b3a7-9325609be5f3","year":2022},"citing_paper":{"arxiv_id":"2411.10957","last_updated":"2024-11-17T04:23:25Z","snapshot_observed_at":"2026-08-13T09:21:09.961803Z","submitted_at":"2024-11-17T04:23:25Z","title":"IMPaCT GNN: Imposing invariance with Message Passing in Chronological split Temporal Graphs","version":1},"reference_index":2010,"source":"pdf_text","source_observed_at":"2026-08-12T19:12:28.971752Z"},"links":{"citing_paper":"/paper/2411.10957"},"observation_digest":"sha256:9ed483d2e858e3605eaff9cf0cca238877613a702fa31f863873b8fd3b93b359","observation_id":"02d1c187-d27d-4e88-9f76-4341a23f1971","resolution":{"observed_at":"2026-08-12T19:12:29.210893Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T19:12:28.967726Z","title":"Temporal graph neural networks for social recommendation","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.10957","last_updated":"2024-11-17T04:23:25Z","snapshot_observed_at":"2026-08-13T09:21:09.961803Z","submitted_at":"2024-11-17T04:23:25Z","title":"IMPaCT GNN: Imposing invariance with Message Passing in Chronological split Temporal Graphs","version":1},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-12T19:12:28.967726Z"},"links":{"citing_paper":"/paper/2411.10957"},"observation_digest":"sha256:9ac867b90e3a15f39864036613b17375e53ff77adbca4df3f6d6eb28ddd5afcd","observation_id":"c7b96676-a6ad-4643-9d72-cb1ff2d115b1","resolution":{"observed_at":"2026-08-12T19:12:28.967726Z","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":"2026-08-12T19:12:29.193997Z","title":"Theoretical analysis of domain adaptation with optimal transport","venue":null,"work_id":"c690e779-dbe6-449d-bd0b-de5e548c58c7","year":2017},"citing_paper":{"arxiv_id":"2411.10957","last_updated":"2024-11-17T04:23:25Z","snapshot_observed_at":"2026-08-13T09:21:09.961803Z","submitted_at":"2024-11-17T04:23:25Z","title":"IMPaCT GNN: Imposing invariance with Message Passing in Chronological split Temporal Graphs","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-12T19:12:28.980151Z"},"links":{"citing_paper":"/paper/2411.10957"},"observation_digest":"sha256:d59af2a984829b5559608b35325dfa94c40631b28d47a6479cb421b5b2e50bc5","observation_id":"3b549dac-4dbe-4e11-8c0a-37f9f829b9bb","resolution":{"observed_at":"2026-08-12T19:12:29.198360Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T19:12:29.127177Z","title":"approximate of expectation","venue":null,"work_id":"1fddeaab-be31-4979-9d20-9a6dbbb4ecb6","year":2023},"citing_paper":{"arxiv_id":"2411.10957","last_updated":"2024-11-17T04:23:25Z","snapshot_observed_at":"2026-08-13T09:21:09.961803Z","submitted_at":"2024-11-17T04:23:25Z","title":"IMPaCT GNN: Imposing invariance with Message Passing in Chronological split Temporal Graphs","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-12T19:12:29.015643Z"},"links":{"citing_paper":"/paper/2411.10957"},"observation_digest":"sha256:1d80227eaa2a5a0bc2b83c11fd98ac3fa9e75096004fe5fb8c15c007aa86f97a","observation_id":"a739f1e5-f047-41f4-9641-b68d56dbe3fb","resolution":{"observed_at":"2026-08-12T19:12:29.131034Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.18875","last_updated":"2024-02-29T05:44:41Z","snapshot_observed_at":"2026-08-13T04:07:39.572692Z","submitted_at":"2024-02-29T05:44:41Z","title":"Loss-aware Curriculum Learning for Heterogeneous Graph Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.18875","snapshot_observed_at":"2026-08-12T19:12:28.994090Z","title":"Loss-aware curriculum learning for heterogeneous graph neural networks","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.10957","last_updated":"2024-11-17T04:23:25Z","snapshot_observed_at":"2026-08-13T09:21:09.961803Z","submitted_at":"2024-11-17T04:23:25Z","title":"IMPaCT GNN: Imposing invariance with Message Passing in Chronological split Temporal Graphs","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-12T19:12:28.994090Z"},"links":{"cited_paper":"/paper/2402.18875","citing_paper":"/paper/2411.10957"},"observation_digest":"sha256:ca0df8595deb547d9eedf5c1be993aedac702f88cdf39bc11ce208a228cf5b82","observation_id":"76805763-43db-45dc-860f-a275c1e929bb","resolution":{"observed_at":"2026-08-12T19:12:28.994090Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2411.10957","last_updated":"2024-11-17T04:23:25Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-13T09:21:09.961803Z","submitted_at":"2024-11-17T04:23:25Z","title":"IMPaCT GNN: Imposing invariance with Message Passing in Chronological split Temporal Graphs"},"reference_resolution":{"displayed":13,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":4,"verified_exact":2,"verified_fuzzy":7},"total_outbound_references":13},"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":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:2411.10957."}