{"as_of":"2026-08-16T10:46:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2001dd8572f4d37eb1aa6854afdb77e2d44a2c6955f6cdf0a27ff1fb21716ddc","coverage":[{"denominator":16,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":16,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T13:23:02.876298Z","state":"measured"},{"denominator":16,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":16,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+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/1908.05365/citation-record","integrity":"/paper/1908.05365/integrity","json":"/paper/1908.05365/citation-record.json","paper":"/paper/1908.05365"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T13:23:03.463133Z","title":"B.3 Network Structure In order to assign each of the∼160M yellow taxi rides to a pair of vertices in our graph, we must map their pickup and drop-oﬀ locations to a census block","venue":null,"work_id":"98aa0284-0be8-4034-a4f9-d45ae39569c9","year":2012},"citing_paper":{"arxiv_id":"1908.05365","last_updated":"2021-01-24T20:28:53Z","snapshot_observed_at":"2026-08-15T08:14:16.627501Z","submitted_at":"2019-08-14T22:38:18Z","title":"End-to-End Learning from Complex Multigraphs with Latent-Graph Convolutional Networks","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T13:23:02.756577Z"},"links":{"citing_paper":"/paper/1908.05365"},"observation_digest":"sha256:067ae78da7ed167dec0d04ddef739c2cd07b4eb5ba69927251f5317772872c43","observation_id":"c4827670-181b-4bee-94e1-47e4ce8dd0ab","resolution":{"observed_at":"2026-08-14T13:23:03.468766Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:23:03.348734Z","title":null,"venue":null,"work_id":"27a8032b-1ad7-4dbd-82fc-abc6ec64fc2c","year":null},"citing_paper":{"arxiv_id":"1908.05365","last_updated":"2021-01-24T20:28:53Z","snapshot_observed_at":"2026-08-15T08:14:16.627501Z","submitted_at":"2019-08-14T22:38:18Z","title":"End-to-End Learning from Complex Multigraphs with Latent-Graph Convolutional Networks","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T13:23:02.805837Z"},"links":{"citing_paper":"/paper/1908.05365"},"observation_digest":"sha256:60ae8cdd7b0d9b86a321fe752fdcc6c0892205cf278841113c70736515658972","observation_id":"f9e5ee98-8a02-4f03-8313-7aedc33e5c05","resolution":{"observed_at":"2026-08-14T13:23:03.363439Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:23:03.509027Z","title":null,"venue":null,"work_id":"399f007c-79ad-40a0-a2bb-3b21a59c4836","year":null},"citing_paper":{"arxiv_id":"1908.05365","last_updated":"2021-01-24T20:28:53Z","snapshot_observed_at":"2026-08-15T08:14:16.627501Z","submitted_at":"2019-08-14T22:38:18Z","title":"End-to-End Learning from Complex Multigraphs with Latent-Graph Convolutional Networks","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T13:23:02.735686Z"},"links":{"citing_paper":"/paper/1908.05365"},"observation_digest":"sha256:7505e332e3f772a47b3a1880ebba5ff00e7b9b47ad0253f48e02ef9cbc7bdbc2","observation_id":"8556a753-62da-4456-bd64-d6385a9fc3b2","resolution":{"observed_at":"2026-08-14T13:23:03.514088Z","resolver_source":"raw_fallback","status":"parse_uncertain"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:23:03.480275Z","title":"Hermsen, Peter Bloem, F abian Jansen & W olf B.W","venue":null,"work_id":"874a669b-99c5-4eb7-95cc-9a8a1c3dc834","year":2012},"citing_paper":{"arxiv_id":"1908.05365","last_updated":"2021-01-24T20:28:53Z","snapshot_observed_at":"2026-08-15T08:14:16.627501Z","submitted_at":"2019-08-14T22:38:18Z","title":"End-to-End Learning from Complex Multigraphs with Latent-Graph Convolutional Networks","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T13:23:02.745582Z"},"links":{"citing_paper":"/paper/1908.05365"},"observation_digest":"sha256:e6e6907eb3df3814a8050094407d4cc8f877be03464d74a19a390754f99354ff","observation_id":"9d1fbd17-cdaf-4178-b0d1-e4374c7cffc7","resolution":{"observed_at":"2026-08-14T13:23:03.493011Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:23:03.383389Z","title":null,"venue":null,"work_id":"6a3b9c8b-7673-47de-8a4d-bb58375a5fb6","year":null},"citing_paper":{"arxiv_id":"1908.05365","last_updated":"2021-01-24T20:28:53Z","snapshot_observed_at":"2026-08-15T08:14:16.627501Z","submitted_at":"2019-08-14T22:38:18Z","title":"End-to-End Learning from Complex Multigraphs with Latent-Graph Convolutional Networks","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T13:23:02.799135Z"},"links":{"citing_paper":"/paper/1908.05365"},"observation_digest":"sha256:4bf95dd3a6e289cc740c3f39952cdb59817497ac2a290645e220eb108cb74a1e","observation_id":"afa64609-8a47-4b0c-8376-d7d5696fdc18","resolution":{"observed_at":"2026-08-14T13:23:03.393302Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:23:03.415589Z","title":"Floris A.W","venue":null,"work_id":"1f6be9e3-36ab-496c-a47d-ce4be286594a","year":2016},"citing_paper":{"arxiv_id":"1908.05365","last_updated":"2021-01-24T20:28:53Z","snapshot_observed_at":"2026-08-15T08:14:16.627501Z","submitted_at":"2019-08-14T22:38:18Z","title":"End-to-End Learning from Complex Multigraphs with Latent-Graph Convolutional Networks","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T13:23:02.785279Z"},"links":{"citing_paper":"/paper/1908.05365"},"observation_digest":"sha256:2d71575c93d9c4a7fb8f32aa681bcccbb8b5a0c1ee785cef9a65874cf522a8c1","observation_id":"01e092b8-88a4-4daa-b5c0-2652911ff059","resolution":{"observed_at":"2026-08-14T13:23:03.426198Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:23:03.323458Z","title":"Types 1 and 2 receive additional small, randomized oﬀsets on their time attributes in order to introduce a degree of noise","venue":null,"work_id":"15395ec8-1a8e-4f11-a9d4-420f4723d943","year":null},"citing_paper":{"arxiv_id":"1908.05365","last_updated":"2021-01-24T20:28:53Z","snapshot_observed_at":"2026-08-15T08:14:16.627501Z","submitted_at":"2019-08-14T22:38:18Z","title":"End-to-End Learning from Complex Multigraphs with Latent-Graph Convolutional Networks","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T13:23:02.812884Z"},"links":{"citing_paper":"/paper/1908.05365"},"observation_digest":"sha256:88363f4b61096856f7554997359de0fc13a56a5504602bf79de92a1aa0b3a4ff","observation_id":"8b1d048d-4549-4783-b5ea-ff81347212b9","resolution":{"observed_at":"2026-08-14T13:23:03.330368Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:23:03.293299Z","title":null,"venue":null,"work_id":"3aed7436-eb94-40ad-aac0-f602c69bb3fe","year":null},"citing_paper":{"arxiv_id":"1908.05365","last_updated":"2021-01-24T20:28:53Z","snapshot_observed_at":"2026-08-15T08:14:16.627501Z","submitted_at":"2019-08-14T22:38:18Z","title":"End-to-End Learning from Complex Multigraphs with Latent-Graph Convolutional Networks","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T13:23:02.821595Z"},"links":{"citing_paper":"/paper/1908.05365"},"observation_digest":"sha256:b53976c07b91995179bb1f811cd668d74ff5728f1ff7f46a43dc17827e744768","observation_id":"195f24d2-aeda-445d-98c2-50b7215ed944","resolution":{"observed_at":"2026-08-14T13:23:03.300482Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:23:03.259308Z","title":null,"venue":null,"work_id":"3d2f0175-95a3-481d-b81a-40b301cc41ef","year":null},"citing_paper":{"arxiv_id":"1908.05365","last_updated":"2021-01-24T20:28:53Z","snapshot_observed_at":"2026-08-15T08:14:16.627501Z","submitted_at":"2019-08-14T22:38:18Z","title":"End-to-End Learning from Complex Multigraphs with Latent-Graph Convolutional Networks","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T13:23:02.828006Z"},"links":{"citing_paper":"/paper/1908.05365"},"observation_digest":"sha256:639ef4d965cd1a9d1b411326b4d5694f923c525202b65205131cc7b735846181","observation_id":"ae52b278-210b-45b2-9433-ba042ac553ff","resolution":{"observed_at":"2026-08-14T13:23:03.266358Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:23:03.233799Z","title":"In case vi∈ N andvj∈ F we introduce fraud type B, having similar but opposite eﬀects:","venue":null,"work_id":"1e3de9d6-e6eb-45e6-93db-edaf8c1c726a","year":null},"citing_paper":{"arxiv_id":"1908.05365","last_updated":"2021-01-24T20:28:53Z","snapshot_observed_at":"2026-08-15T08:14:16.627501Z","submitted_at":"2019-08-14T22:38:18Z","title":"End-to-End Learning from Complex Multigraphs with Latent-Graph Convolutional Networks","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T13:23:02.839599Z"},"links":{"citing_paper":"/paper/1908.05365"},"observation_digest":"sha256:26cc390204de50c8ba9e025e193e3637e7279cea18ddc08eae996fd3aa5a9580","observation_id":"50be4929-87c0-4222-bbb5-0bb756919e50","resolution":{"observed_at":"2026-08-14T13:23:03.243062Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:23:03.197815Z","title":null,"venue":null,"work_id":"0772ddf9-a1a7-49a1-b51d-42b01a9ae830","year":null},"citing_paper":{"arxiv_id":"1908.05365","last_updated":"2021-01-24T20:28:53Z","snapshot_observed_at":"2026-08-15T08:14:16.627501Z","submitted_at":"2019-08-14T22:38:18Z","title":"End-to-End Learning from Complex Multigraphs with Latent-Graph Convolutional Networks","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T13:23:02.848777Z"},"links":{"citing_paper":"/paper/1908.05365"},"observation_digest":"sha256:14a4854e8a1a649487ce102ff3e6e94a384332568ab15de3c8c2111209fb8c84","observation_id":"bba76970-7a43-4944-9b47-0b43c02364b7","resolution":{"observed_at":"2026-08-14T13:23:03.207802Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:23:03.163576Z","title":null,"venue":null,"work_id":"0baf8223-ae38-486c-bf55-70e9e23fe36a","year":null},"citing_paper":{"arxiv_id":"1908.05365","last_updated":"2021-01-24T20:28:53Z","snapshot_observed_at":"2026-08-15T08:14:16.627501Z","submitted_at":"2019-08-14T22:38:18Z","title":"End-to-End Learning from Complex Multigraphs with Latent-Graph Convolutional Networks","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T13:23:02.856781Z"},"links":{"citing_paper":"/paper/1908.05365"},"observation_digest":"sha256:30487157f918f8b5631750ee0306b1acb2690231b824e7bbfc5cbb3a4626ca03","observation_id":"994953ac-9afc-422a-b451-24a43cc859b4","resolution":{"observed_at":"2026-08-14T13:23:03.172644Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:23:03.122592Z","title":"11All of these modiﬁcations take place with a per- transaction probability of 1/3","venue":null,"work_id":"6478e369-286c-4bfb-9325-a34f12fccfdb","year":null},"citing_paper":{"arxiv_id":"1908.05365","last_updated":"2021-01-24T20:28:53Z","snapshot_observed_at":"2026-08-15T08:14:16.627501Z","submitted_at":"2019-08-14T22:38:18Z","title":"End-to-End Learning from Complex Multigraphs with Latent-Graph Convolutional Networks","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T13:23:02.867730Z"},"links":{"citing_paper":"/paper/1908.05365"},"observation_digest":"sha256:36f437da37691dfea1e4286b76f8050acfac7e677eb89397f03134ebeccb26c7","observation_id":"ff16a6f9-3424-49d1-b5b5-f17da2ce3f36","resolution":{"observed_at":"2026-08-14T13:23:03.138902Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:23:03.052981Z","title":"Values for|E| also diﬀer slightly because of the removal of generated self-connections","venue":null,"work_id":"7761c9be-e01c-4926-b73d-868c1d589719","year":null},"citing_paper":{"arxiv_id":"1908.05365","last_updated":"2021-01-24T20:28:53Z","snapshot_observed_at":"2026-08-15T08:14:16.627501Z","submitted_at":"2019-08-14T22:38:18Z","title":"End-to-End Learning from Complex Multigraphs with Latent-Graph Convolutional Networks","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T13:23:02.876298Z"},"links":{"citing_paper":"/paper/1908.05365"},"observation_digest":"sha256:620bf54217c4c40a62f4b4676110e49caddf5d41c9ca455f5ae03fe9828d6aa0","observation_id":"3bf0e3d8-3450-4cde-a658-fad725f7e9ca","resolution":{"observed_at":"2026-08-14T13:23:03.093371Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.01261","last_updated":"2018-10-17T17:51:36Z","snapshot_observed_at":"2026-08-15T04:21:38.631648Z","submitted_at":"2018-06-04T17:58:18Z","title":"Relational inductive biases, deep learning, and graph networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1806.01261","snapshot_observed_at":"2026-08-14T13:23:02.712761Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.05365","last_updated":"2021-01-24T20:28:53Z","snapshot_observed_at":"2026-08-15T08:14:16.627501Z","submitted_at":"2019-08-14T22:38:18Z","title":"End-to-End Learning from Complex Multigraphs with Latent-Graph Convolutional Networks","version":2},"reference_index":2002,"source":"pdf_text","source_observed_at":"2026-08-14T13:23:02.712761Z"},"links":{"cited_paper":"/paper/1806.01261","citing_paper":"/paper/1908.05365"},"observation_digest":"sha256:63e678ed171bbfc8fa2f32001b87695e6dec83248543b0a057daf40f0d8ddb56","observation_id":"2b8c21f7-ece0-4f53-89ac-69b1395c4a3b","resolution":{"observed_at":"2026-08-14T13:23:02.712761Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1606.09375","last_updated":"2017-02-05T17:04:39Z","snapshot_observed_at":"2026-08-14T21:50:28.401426Z","submitted_at":"2016-06-30T07:42:13Z","title":"Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1606.09375","snapshot_observed_at":"2026-08-14T13:23:02.722286Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1908.05365","last_updated":"2021-01-24T20:28:53Z","snapshot_observed_at":"2026-08-15T08:14:16.627501Z","submitted_at":"2019-08-14T22:38:18Z","title":"End-to-End Learning from Complex Multigraphs with Latent-Graph Convolutional Networks","version":2},"reference_index":2016,"source":"pdf_text","source_observed_at":"2026-08-14T13:23:02.722286Z"},"links":{"cited_paper":"/paper/1606.09375","citing_paper":"/paper/1908.05365"},"observation_digest":"sha256:ec9c3f9e2723f0823a478df8c465559ab12cb4c7a18fce903f12eb5dbc9e9224","observation_id":"605e780c-8573-4236-95c1-4e1460bca8c9","resolution":{"observed_at":"2026-08-14T13:23:02.722286Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"1908.05365","last_updated":"2021-01-24T20:28:53Z","latest_version":2,"primary_category":"stat.ML","snapshot_observed_at":"2026-08-15T08:14:16.627501Z","submitted_at":"2019-08-14T22:38:18Z","title":"End-to-End Learning from Complex Multigraphs with Latent-Graph Convolutional Networks"},"reference_resolution":{"displayed":16,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":1,"unresolved":8,"verified_exact":0,"verified_fuzzy":7},"total_outbound_references":16},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:1908.05365."}