{"as_of":"2026-08-15T04:50:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8c9e3038a7d3bbef2475b84320364f0542a04c11a1f3f004eea242e9bcf60239","coverage":[{"denominator":55,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":55,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T18:38:42.694233Z","state":"measured"},{"denominator":55,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":55,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+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/2411.11375/citation-record","integrity":"/paper/2411.11375/integrity","json":"/paper/2411.11375/citation-record.json","paper":"/paper/2411.11375"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:38:42.305512Z","title":"Fineman, Matteo Frigo, John R","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.305512Z"},"links":{"citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:de84c40de20f74ce54d7bb25fdb843a5f761c3b23a4b5dd705e8eb7a11037f63","observation_id":"1c2f4fd3-5a36-4470-a0b7-ae8c28a2e108","resolution":{"observed_at":"2026-08-12T18:38:42.305512Z","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":"2026-08-12T18:38:44.422086Z","title":"Hamilton, Zhitao Ying, and Jure Leskovec","venue":null,"work_id":"15c09988-572f-4cf9-b2f2-95bca025dcfa","year":2017},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.308653Z"},"links":{"citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:5d37f4c33b1db113a9f8df2a6852ffc5c34994779bc76e96467576a608909873","observation_id":"dcf7a525-0f62-478f-af5e-051980f60d4c","resolution":{"observed_at":"2026-08-12T18:38:44.424714Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1801.10247","last_updated":"2018-01-30T22:36:16Z","snapshot_observed_at":"2026-08-14T19:50:30.215861Z","submitted_at":"2018-01-30T22:36:16Z","title":"FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.10247","snapshot_observed_at":"2026-08-12T18:38:42.311355Z","title":"Fastgcn: fast learning with graph convolutional networks via importance sampling","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.311355Z"},"links":{"cited_paper":"/paper/1801.10247","citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:895e45023a78654422b216c656f95679381e896bf2c58a23d0480ebf09e8af9b","observation_id":"5e91731e-a180-4bbc-b48a-9f4e3a74f6a0","resolution":{"observed_at":"2026-08-12T18:38:42.311355Z","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":"2026-08-12T18:38:44.414838Z","title":"Layer- dependent importance sampling for training deep and large graph convolutional networks","venue":null,"work_id":"56f191fc-a66a-4f9d-bd48-168c5df30e0a","year":2019},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.314414Z"},"links":{"citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:7558bb3373ce8d17c89e7a14235fae4de01c0140c7c7bec3029cb39f462cce60","observation_id":"997d24c1-d322-45a5-aa2c-630708438f3c","resolution":{"observed_at":"2026-08-12T18:38:44.417933Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-12T18:38:44.345308Z","title":"Distdgl: Distributed graph neural network training for billion-scale graphs","venue":null,"work_id":"5c32136a-4329-42bd-8407-1df9d0766b36","year":2020},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.317153Z"},"links":{"citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:c1f20d28f9c9fef2ca2bacb0546c40a1d16641950c3d9874f4527188767e8ff4","observation_id":"c987b7fb-372e-4b42-869b-d82f70a005c9","resolution":{"observed_at":"2026-08-12T18:38:44.385352Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-12T18:38:42.320000Z","title":"Pytorch distributed: experiences on accelerating data parallel training","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.320000Z"},"links":{"citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:961d673fa060ff79e87d9e42b8c70cb0df7ceabc301ebf1ce282c040d85fb995","observation_id":"54bf8e2a-d90d-4bd5-b89e-81ef27bbd56d","resolution":{"observed_at":"2026-08-12T18:38:42.320000Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1903.02428","last_updated":"2019-04-25T10:06:09Z","snapshot_observed_at":"2026-08-02T18:54:43.326912Z","submitted_at":"2019-03-06T14:50:02Z","title":"Fast Graph Representation Learning with PyTorch Geometric","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1903.02428","snapshot_observed_at":"2026-08-12T18:38:42.322788Z","title":"Fast graph representation learning with pytorch geometric","venue":null,"work_id":null,"year":1903},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.322788Z"},"links":{"cited_paper":"/paper/1903.02428","citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:49325c61d9fe0102e05515ebf3bd8e8a3351b8da166b9498f8175927ab2531a6","observation_id":"8224a6c2-19fa-46db-a9fd-95af08bdf3b3","resolution":{"observed_at":"2026-08-12T18:38:42.322788Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1909.01315","last_updated":"2020-08-25T15:46:13Z","snapshot_observed_at":"2026-07-06T08:18:46.549511Z","submitted_at":"2019-09-03T17:10:28Z","title":"Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.01315","snapshot_observed_at":"2026-08-12T18:38:42.325451Z","title":"Deep graph library: A graph-centric, highly-performant package for graph neural networks","venue":null,"work_id":null,"year":1909},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.325451Z"},"links":{"cited_paper":"/paper/1909.01315","citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:1026d3d4fe0d36de0e6803a6dec4946cbbd0abf54ce5a3e26b40d0ce21c4c782","observation_id":"72ead72a-b4ee-4464-a972-604169f5ac29","resolution":{"observed_at":"2026-08-12T18:38:42.325451Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.03522","last_updated":"2023-07-23T21:18:29Z","snapshot_observed_at":"2026-08-13T15:08:40.449974Z","submitted_at":"2022-07-07T18:34:34Z","title":"TF-GNN: Graph Neural Networks in TensorFlow","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.03522","snapshot_observed_at":"2026-08-12T18:38:42.331966Z","title":"TF-GNN: graph neural networks in tensorflow","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.331966Z"},"links":{"cited_paper":"/paper/2207.03522","citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:8164884a958da4f8e49eeaa77a8a3f2cd4740e3045b4073b600a60876fc5dfd6","observation_id":"7ce009ce-00ca-4946-bcf8-82de9ec7fe09","resolution":{"observed_at":"2026-08-12T18:38:42.331966Z","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":"2026-08-12T18:38:44.293074Z","title":"A fast and high quality multilevel scheme for parti- tioning irregular graphs","venue":null,"work_id":"59044afd-ccb2-45f2-9237-d7c2a2fd31eb","year":1998},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.345131Z"},"links":{"citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:bdd03598961bdd161540feba60aedbc6b2c8bb416034114b0b72f405846b7693","observation_id":"a2e87686-728f-4106-90c3-b5dab39408c7","resolution":{"observed_at":"2026-08-12T18:38:44.298158Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-12T18:38:44.286152Z","title":"METIS: A Software Package for Partitioning Unstructured Graphs, Partitioning Meshes, and Computing Fill-Reducing Orderings of Sparse Matrices , September 1998","venue":null,"work_id":"884ce330-6057-426e-aefa-a615e8e51937","year":1998},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.350886Z"},"links":{"citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:3be9b1aa06e7b14708ca5164d6f6bbc1f6a9dcf2813de408f32c3a176aff89dc","observation_id":"6deeb626-e1aa-469c-be75-e9f67ab7091c","resolution":{"observed_at":"2026-08-12T18:38:44.288626Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2308.03209","last_updated":"2023-08-06T21:04:58Z","snapshot_observed_at":"2026-08-14T04:52:09.223876Z","submitted_at":"2023-08-06T21:04:58Z","title":"Communication-Free Distributed GNN Training with Vertex Cut","version":1},"cited_work":{"arxiv_id":"2308.03209","doi":null,"metadata_source":"pith","pith_arxiv_id":"2308.03209","snapshot_observed_at":"2026-08-12T18:38:43.136716Z","title":"Communication-Free Distributed GNN Training with Vertex Cut","venue":"cs.LG","work_id":"429c2c79-1853-4ee4-aeb0-2d344482cb65","year":2023},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.367631Z"},"links":{"cited_paper":"/paper/2308.03209","citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:1df6dc21ffc75023559d73766e1a3e012874be18d1126264aea30fb0e70f80d2","observation_id":"4bed9239-a0d0-4ad6-ae8d-87d7111b7a6d","resolution":{"observed_at":"2026-08-12T18:38:43.223829Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-12T18:38:44.279198Z","title":"Scalable and efficient full-graph gnn training for large graphs","venue":null,"work_id":"e3330e4b-4a73-41d2-89d3-887b0057d55c","year":2023},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.384663Z"},"links":{"citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:ed983495c0f1dafa2e141069a80f3bf745ba6f19ee7aa55707e2aa0044290b36","observation_id":"1850d665-1ac0-4a0a-8b9c-9aa520c6d4bb","resolution":{"observed_at":"2026-08-12T18:38:44.282007Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-12T18:38:44.271756Z","title":"Bytegnn: efficient graph neural network training at large scale","venue":null,"work_id":"22090cac-2056-4be3-8a31-c922a02f7ea4","year":2022},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.387423Z"},"links":{"citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:97de309cc8d59e839ece2bb3d8cb4bdff2fb126dc5c56bbd92d63e8a3b0cc9f7","observation_id":"aeed18b6-ceeb-4720-9230-afd3d4576be3","resolution":{"observed_at":"2026-08-12T18:38:44.275021Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1610.06264","last_updated":"2017-06-15T19:31:04Z","snapshot_observed_at":"2026-08-14T21:34:24.646723Z","submitted_at":"2016-10-20T01:59:48Z","title":"Foundations of Modern Query Languages for Graph Databases","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1610.06264","snapshot_observed_at":"2026-08-12T18:38:42.389349Z","title":"Foundations of modern query languages for graph databases, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.389349Z"},"links":{"cited_paper":"/paper/1610.06264","citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:929a1e9f0bfa80f9a306a617dd2dc8775ac3c4352831aa4de713e7907444e656","observation_id":"4f72a670-62e6-44bf-9b82-14024daf0c53","resolution":{"observed_at":"2026-08-12T18:38:42.389349Z","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":"2026-08-12T18:38:44.263642Z","title":"https://www.w3.org/RDF/, 2014","venue":null,"work_id":"d7a022f5-bae7-45fa-a2f9-e84a845e5a5f","year":2014},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.392296Z"},"links":{"citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:ae55d3b6237efae07538e11fa8b4170128adbb2d65dfe40e1f89d5477f233f18","observation_id":"d5102af1-a9d4-4f07-aa43-e3797f9dc8a4","resolution":{"observed_at":"2026-08-12T18:38:44.267268Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-12T18:38:44.256364Z","title":"Automating the construction of internet portals with machine learning","venue":null,"work_id":"05fd034e-b6fe-4402-9881-f6f245556e3a","year":null},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.394801Z"},"links":{"citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:f43f4757f0dda67defeb78dc8bf19c00786390fb817a1c586ffcdf30a68576a6","observation_id":"dab1ddd9-82b0-48a3-8e9d-7896c01dcc99","resolution":{"observed_at":"2026-08-12T18:38:44.259352Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-12T18:38:44.249012Z","title":"Open graph benchmark: Datasets for machine learning on graphs","venue":null,"work_id":"626a34d5-c34a-43fa-b403-77f003692bb0","year":2020},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.399284Z"},"links":{"citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:8e4c67867b97f8591de185ee98b05cfe3d58e396fb136666bcb1cfdbd939d047","observation_id":"45f9ab14-52d1-4b83-a06e-e1eb929fdd9b","resolution":{"observed_at":"2026-08-12T18:38:44.251929Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-12T18:38:42.401821Z","title":"Cypher: An evolving query language for property graphs","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.401821Z"},"links":{"citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:6a840bdf235d1a0d8e640695e07d6dafee80fc5cb344b37dd83b55b4e7e790d5","observation_id":"d94295a1-3e6d-4c71-a41d-ab4e77a389ea","resolution":{"observed_at":"2026-08-12T18:38:42.401821Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1802.09984","last_updated":"2018-03-20T18:27:52Z","snapshot_observed_at":"2026-08-14T19:41:34.601179Z","submitted_at":"2018-02-27T16:01:36Z","title":"Formal Semantics of the Language Cypher","version":2},"cited_work":{"arxiv_id":"1802.09984","doi":null,"metadata_source":"pith","pith_arxiv_id":"1802.09984","snapshot_observed_at":"2026-08-12T18:38:43.074257Z","title":"Formal Semantics of the Language Cypher","venue":"cs.DB","work_id":"4e03fcb6-734d-4c52-bf32-b6729612a4b0","year":2018},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.404340Z"},"links":{"cited_paper":"/paper/1802.09984","citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:707ea6cc8f3d9dd16433675361e4baeb5364776a0ad97791191e0d2e997ca0ac","observation_id":"b4f8c4af-c2e1-4065-8b28-50b622920231","resolution":{"observed_at":"2026-08-12T18:38:43.077139Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.5441/002/edbt.2018.62","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:38:42.719025Z","title":"opencypher: New directions in property graph querying","venue":null,"work_id":"89957dc2-245d-4d1a-a224-ca7b53f9097d","year":2018},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.406825Z"},"links":{"citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:8050291ee5d013e13b2b755df2e7061eb0825d6e1dfdd629f39060536007cbb5","observation_id":"b28ab08c-4115-4354-8c32-b72899243d8c","resolution":{"observed_at":"2026-08-12T18:38:42.723450Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-12T18:38:44.155494Z","title":"https://www.iso.org/standard/76120.html, 2024","venue":null,"work_id":"ecaf58b4-3016-43e8-a41f-7a19a05fa0d8","year":2024},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.409642Z"},"links":{"citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:3eca10b307bff5832a4627fc6aeb10296b81af9b7d7a4c9374d7ffb7e5bcc0da","observation_id":"70dd9351-043f-4566-8df2-67f2b77d3ec1","resolution":{"observed_at":"2026-08-12T18:38:44.201091Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2112.06217","last_updated":"2021-12-12T12:39:09Z","snapshot_observed_at":"2026-08-13T17:17:27.035196Z","submitted_at":"2021-12-12T12:39:09Z","title":"Graph Pattern Matching in GQL and SQL/PGQ","version":1},"cited_work":{"arxiv_id":"2112.06217","doi":null,"metadata_source":"pith","pith_arxiv_id":"2112.06217","snapshot_observed_at":"2026-08-12T18:38:42.983177Z","title":"Graph Pattern Matching in GQL and SQL/PGQ","venue":"cs.DB","work_id":"5c0717cc-832b-4270-b3f2-fdc50d181bdf","year":2021},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.412244Z"},"links":{"cited_paper":"/paper/2112.06217","citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:9a756aeecbb53effad5224159b67399dfcd8d5f2dd9401f18661dcd8b2eeb23f","observation_id":"35202af8-9761-4577-86c7-d52898645315","resolution":{"observed_at":"2026-08-12T18:38:43.042129Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-12T18:38:44.147486Z","title":"https://neo4j.com/","venue":null,"work_id":"d794d767-7ed1-4102-bcd5-7c5181512442","year":null},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.414849Z"},"links":{"citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:8da60af254bb9dc40978d2bc7431c8420559f9deb2945f56a98436744f83c7e9","observation_id":"7be4963a-8f42-4948-b29d-bf26291e1fad","resolution":{"observed_at":"2026-08-12T18:38:44.150428Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-12T18:38:44.139550Z","title":"https://arangodb.com/","venue":null,"work_id":"029327d3-13d6-4305-ab0c-665f1f0ba2e5","year":null},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.417298Z"},"links":{"citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:ffb239885e1a7eed94363b5b3eb2f6bd86ec37785843ef1337c457a389eb4b7c","observation_id":"9da72c44-bef3-4835-be4d-1afe9a3385f6","resolution":{"observed_at":"2026-08-12T18:38:44.142204Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-12T18:38:44.131094Z","title":"https://www.tigergraph.com/","venue":null,"work_id":"b7b87e93-fd6b-4c52-94f8-eecd6f209d38","year":null},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.419672Z"},"links":{"citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:950bfea722f5f0f4aa8fc0e006af80815dcf997c262bea48393d2cfe43cf86a9","observation_id":"3456bfa7-4821-46f3-a892-f40a208bcdfb","resolution":{"observed_at":"2026-08-12T18:38:44.134182Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-12T18:38:44.122703Z","title":"https://www.w3.org/TR/sparql11-query/, 2013","venue":null,"work_id":"f02801c7-9e6a-49d2-9401-fdfe41a1560f","year":2013},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.422315Z"},"links":{"citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:e147adf59f95b2379c6981136506dd4018c9461e0d9360cecf391cb75aa56c77","observation_id":"240f33bd-4bee-49a3-b86a-4d2f80b80706","resolution":{"observed_at":"2026-08-12T18:38:44.126079Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-12T18:38:44.114839Z","title":"Rdfox: A highly-scalable rdf store","venue":null,"work_id":"c611d8be-e8ea-4d69-89d4-f360d0c740ec","year":2015},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.424789Z"},"links":{"citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:ad04d7418c7260b5030ec9e0478855eb2ba0fe5f74d8ef0b89af268c7dee3f20","observation_id":"c03c84bf-461f-454c-8e22-35be4fa3c1df","resolution":{"observed_at":"2026-08-12T18:38:44.117872Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-12T18:38:44.032931Z","title":"https://aws.amazon.com/neptune/","venue":null,"work_id":"b5daff07-9af8-4ec0-a050-40194ff87071","year":null},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.426816Z"},"links":{"citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:147527b9595b3fb84d38634bdc97e9e400ba3b3d938ca16a351a34b74f798316","observation_id":"990caa4d-56ea-4be7-84c1-c34bd954cab5","resolution":{"observed_at":"2026-08-12T18:38:44.071345Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-12T18:38:43.973433Z","title":"Kùzu: Graph learning applications need a modern graph DBMS","venue":null,"work_id":"e3c105b5-c614-4b9a-9494-d0d9d18a998d","year":null},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.429503Z"},"links":{"citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:188285918dc88f0f6063b3fe896cd085f7c8bbdc7a0295b82324c241622ac64b","observation_id":"665a835a-669f-43d1-9875-bdad76d7476a","resolution":{"observed_at":"2026-08-12T18:38:43.977040Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-12T18:38:43.955761Z","title":"Neural graph databases","venue":null,"work_id":"562496ae-3d1c-4c02-9b6c-8e249f63fb46","year":2022},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.434200Z"},"links":{"citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:db78d66b1c0dedf3c7d1538ff8bed1b63427961fe07888be5b94c465b141f9b7","observation_id":"b92eef40-3e54-4048-b6c3-c9bf1d876cc0","resolution":{"observed_at":"2026-08-12T18:38:43.958989Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-12T18:38:43.947111Z","title":null,"venue":null,"work_id":"b4d2c8d1-507b-4574-ad25-cdc6fd5b22aa","year":2019},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.436670Z"},"links":{"citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:ddb39f3889fc8341298dd5108b3cdad11048ebd8145f6b1b866762cdbaba0255","observation_id":"cde6deec-8f25-4a79-9027-210dd70e341c","resolution":{"observed_at":"2026-08-12T18:38:43.950185Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-12T18:38:43.863402Z","title":"node2vec: Scalable feature learning for networks","venue":null,"work_id":"e6ec92b9-e5c7-45ff-9a7b-009eaefe2b60","year":2016},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.439091Z"},"links":{"citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:870c6e08fb049a49c73a7f806e2cc45b1b6db53d45a40c7c8b198bf216dd6e2d","observation_id":"ff457a81-1ae1-4053-b6ca-5445c63ea648","resolution":{"observed_at":"2026-08-12T18:38:43.941683Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.14617","last_updated":"2023-03-26T04:03:37Z","snapshot_observed_at":"2026-08-13T12:17:01.003110Z","submitted_at":"2023-03-26T04:03:37Z","title":"Neural Graph Reasoning: Complex Logical Query Answering Meets Graph Databases","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.14617","snapshot_observed_at":"2026-08-12T18:38:42.451234Z","title":"Neural graph reasoning: Complex logical query answering meets graph databases","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.451234Z"},"links":{"cited_paper":"/paper/2303.14617","citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:c7c5f83f1826424a1acdc1ae7c49c791544ea9a789815a2af981d9d85f683c7f","observation_id":"fd7ca8c4-f0f7-4eab-af36-2bf1ab11f7ce","resolution":{"observed_at":"2026-08-12T18:38:42.451234Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.04615","last_updated":"2023-12-07T18:51:41Z","snapshot_observed_at":"2026-08-13T05:07:43.941227Z","submitted_at":"2023-12-07T18:51:41Z","title":"Relational Deep Learning: Graph Representation Learning on Relational Databases","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.04615","snapshot_observed_at":"2026-08-12T18:38:42.503601Z","title":"Relational deep learning: Graph representation learning on relational databases","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.503601Z"},"links":{"cited_paper":"/paper/2312.04615","citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:e5fd478e010a013f9ae793cb04edb453423292e0997652337dcbab9985167e7f","observation_id":"cd50fd52-7ed8-4ca7-8fd4-f80c68dddd40","resolution":{"observed_at":"2026-08-12T18:38:42.503601Z","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":"2026-08-12T18:38:43.788799Z","title":"The shift from models to compound ai systems","venue":null,"work_id":"b3c67505-a1de-4116-82dd-156705447d48","year":2024},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.575095Z"},"links":{"citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:a1877d04f7208e8668184fcd2b93232bba36613e401442b664bd1f11e676c7f2","observation_id":"10328bc6-e0d8-4fe9-9be2-328f7d8aa3b2","resolution":{"observed_at":"2026-08-12T18:38:43.822846Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-12T18:38:43.779652Z","title":"Retrieval-augmented generation for knowledge-intensive nlp tasks","venue":null,"work_id":"5e5d8973-1385-4c91-af94-bb6e8e3ce19b","year":2020},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.578281Z"},"links":{"citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:c58a18056f9e673e58f47a78f026ac74ab80c35f214a96fea38ce614b770da43","observation_id":"415248fe-da8a-4244-9720-98b0b3f3df19","resolution":{"observed_at":"2026-08-12T18:38:43.783294Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.16130","last_updated":"2025-02-19T10:49:41Z","snapshot_observed_at":"2026-07-06T18:05:11.700127Z","submitted_at":"2024-04-24T18:38:11Z","title":"From Local to Global: A Graph RAG Approach to Query-Focused Summarization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.16130","snapshot_observed_at":"2026-08-12T18:38:42.580782Z","title":"From local to global: A graph rag approach to query-focused summarization","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.580782Z"},"links":{"cited_paper":"/paper/2404.16130","citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:6c55f148877b49362044bde1ffbf1a647df1e7dbb1518e55368ace40a862b53a","observation_id":"750ba800-dc34-43d5-9fa3-90174dc0e7b4","resolution":{"observed_at":"2026-08-12T18:38:42.580782Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.20139","last_updated":"2024-05-30T15:14:24Z","snapshot_observed_at":"2026-08-14T21:15:35.635532Z","submitted_at":"2024-05-30T15:14:24Z","title":"GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.20139","snapshot_observed_at":"2026-08-12T18:38:42.584532Z","title":"Gnn-rag: Graph neural retrieval for large language model reasoning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.584532Z"},"links":{"cited_paper":"/paper/2405.20139","citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:e49e91e7b33e717648168a7c0c25e6dee6d645e6e43e310b36725dda1c7c110e","observation_id":"8ccfae1a-1a00-4cd7-b9ca-62227accdcf1","resolution":{"observed_at":"2026-08-12T18:38:42.584532Z","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":"2026-08-12T18:38:43.770826Z","title":"Exploration of approaches for in- database ml","venue":null,"work_id":"a08feb73-8917-4545-ab01-62b70187a896","year":2023},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.587125Z"},"links":{"citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:4a0a79d0584782c1b3fa054efff3fd5334cf035bdf2574324cbda56bb49f50a7","observation_id":"3c37bca3-7526-4d61-ba6e-677cbd9f53ea","resolution":{"observed_at":"2026-08-12T18:38:43.774491Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1703.04780","last_updated":"2020-02-06T21:16:32Z","snapshot_observed_at":"2026-08-14T21:11:57.670230Z","submitted_at":"2017-03-14T22:27:09Z","title":"Learning Models over Relational Data using Sparse Tensors and Functional Dependencies","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1703.04780","snapshot_observed_at":"2026-08-12T18:38:42.589634Z","title":"Learning models over relational data using sparse tensors and functional dependencies","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.589634Z"},"links":{"cited_paper":"/paper/1703.04780","citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:a886d3134439d8c667b4acb2d408591229cee1b791d0287a42f1a200d39dbea5","observation_id":"fb4c23a6-5860-4e39-8c1d-2abe439875d9","resolution":{"observed_at":"2026-08-12T18:38:42.589634Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2008.07864","last_updated":"2020-08-18T11:25:45Z","snapshot_observed_at":"2026-08-03T10:05:54.940357Z","submitted_at":"2020-08-18T11:25:45Z","title":"The Relational Data Borg is Learning","version":1},"cited_work":{"arxiv_id":"2008.07864","doi":null,"metadata_source":"pith","pith_arxiv_id":"2008.07864","snapshot_observed_at":"2026-08-12T18:38:42.949181Z","title":"The Relational Data Borg is Learning","venue":"cs.DB","work_id":"c0dff983-9b37-47c2-a0bd-21876a1bd3d9","year":2020},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.592636Z"},"links":{"cited_paper":"/paper/2008.07864","citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:46751292ad7aef3cec8174f7564273cef42072593c134c5e0ec0da47abd00d4f","observation_id":"a511ede3-5e0d-486d-ab2f-e99c6e136992","resolution":{"observed_at":"2026-08-12T18:38:42.951680Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-12T18:38:43.727920Z","title":"https://www.pinecone.io/","venue":null,"work_id":"4e74250c-5152-4413-964e-b3d703a34168","year":null},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.595226Z"},"links":{"citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:53c9c18b1356dece7d7258c01a991fe6960fcd50437c7c1fe5bba88f053c3d56","observation_id":"54d1b672-c3d4-405d-9575-4dde967919fe","resolution":{"observed_at":"2026-08-12T18:38:43.765068Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-12T18:38:43.659830Z","title":"The graph database interface: Scaling online transactional and analytical graph workloads to hundreds of thousands of cores","venue":null,"work_id":"d278c8fd-f524-4bd8-ac81-a1131eac6d2e","year":2023},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.597739Z"},"links":{"citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:71146199919952b2f6bc940b50a81f9c9fb01130f4f34008d1057de5a1113b22","observation_id":"0ce8fc9b-ec22-4aeb-8958-5c3fb9c78edc","resolution":{"observed_at":"2026-08-12T18:38:43.664258Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-12T18:38:43.649994Z","title":"Powerlyra: Differentiated graph computation and partitioning on skewed graphs","venue":null,"work_id":"60e59f71-9a68-458a-8b5b-306706fc9ab0","year":2019},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.600229Z"},"links":{"citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:5998d838d88f8f7d2a51180c8cc9d79828e57c2c9cfaa026d310a1a51aadfe0a","observation_id":"b28c3f29-fe4d-4887-bc75-6f0b47ecc8a7","resolution":{"observed_at":"2026-08-12T18:38:43.654277Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2105.04449","last_updated":"2021-05-11T11:13:11Z","snapshot_observed_at":"2026-08-13T19:25:22.555671Z","submitted_at":"2021-05-10T15:18:27Z","title":"G-Tran: Making Distributed Graph Transactions Fast","version":2},"cited_work":{"arxiv_id":"2105.04449","doi":null,"metadata_source":"pith","pith_arxiv_id":"2105.04449","snapshot_observed_at":"2026-08-12T18:38:42.840556Z","title":"G-Tran: Making Distributed Graph Transactions Fast","venue":"cs.DB","work_id":"45911252-3466-40de-ab61-1ff9c0f7c7dd","year":2021},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.638625Z"},"links":{"cited_paper":"/paper/2105.04449","citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:5157b41e778b4af10b0d26d5336a4e5451e7d854fad08ed5e688b00ab4fcc468","observation_id":"c0ff9aee-9e97-4599-abf6-a1115867c6ff","resolution":{"observed_at":"2026-08-12T18:38:42.902830Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-12T18:38:43.639157Z","title":"Kùzu graph database management system","venue":null,"work_id":"358c4aa3-dca5-4cf7-a276-7d2b60991ffc","year":2023},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.656460Z"},"links":{"citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:a73a77221b1e0cb59fcf036c3217f596391a4a962a5b1cf746bdfedbf8f83ca2","observation_id":"7acfdbf3-a14e-453c-adff-097bb64835ed","resolution":{"observed_at":"2026-08-12T18:38:43.643459Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-12T18:38:43.498326Z","title":"https://graph500.org/","venue":null,"work_id":"c8671aeb-309f-4684-b92a-bcbd1168ffe3","year":null},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.668102Z"},"links":{"citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:8eaef4a9a484a2d8c07bf7cb5329b66cb065950089bba401dd89ab44438fc2f3","observation_id":"13a51a4d-b468-438c-b9d0-577224de49d5","resolution":{"observed_at":"2026-08-12T18:38:43.536493Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-12T18:38:43.489841Z","title":"Sampling meth- ods for efficient training of graph convolutional networks: A survey","venue":null,"work_id":"7ac88b1e-3603-4fa5-a6d9-05ed8f0b98f5","year":2021},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.683594Z"},"links":{"citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:e7301d707af886907a9201703ff3234e886fe6c44cf983b0c6752c1b480c7ca2","observation_id":"21eafc5d-b501-45d1-b8de-25c82f958bd8","resolution":{"observed_at":"2026-08-12T18:38:43.492642Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-12T18:38:43.480381Z","title":"Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding","venue":null,"work_id":"a062c289-db3d-4c32-8329-39ade1555b24","year":2020},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.686968Z"},"links":{"citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:248c08e544a095d3c971d4491254f317a8e7c8cfabbb06c83c87d0011e4f11cc","observation_id":"9bb92ab9-f0da-4fc1-af8a-40660135d7cf","resolution":{"observed_at":"2026-08-12T18:38:43.483158Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-12T18:38:43.393294Z","title":"Het- erogeneous graph attention network","venue":null,"work_id":"96fd0acf-fe8a-4497-93ea-5b3606dc10e5","year":2022},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.690217Z"},"links":{"citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:d9deaa2666cbbd71b3420fbb25ece5ca8ecb5e50bf7aa21371e9c2525ba01f34","observation_id":"4225fd78-2bb3-42fa-b8d8-c3f3ea00d3b4","resolution":{"observed_at":"2026-08-12T18:38:43.474970Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-12T18:38:42.692186Z","title":"Chawla, and Ananthram Swami","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.692186Z"},"links":{"citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:e65996ae401f063461ad99f753887c529113a48215b5dd216e17fb9f390b45eb","observation_id":"8e528a4f-af08-411a-8d03-16816334c74a","resolution":{"observed_at":"2026-08-12T18:38:42.692186Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:38:42.694233Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.694233Z"},"links":{"citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:1196ab220d2f2b3d078e5ce9309432c3450ade1294f24b3446cdda3e6e36066e","observation_id":"9ef27059-9db2-460f-b841-ac3a49a5dfbd","resolution":{"observed_at":"2026-08-12T18:38:42.694233Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:38:42.397126Z","title":"doi: 10.1023/A:1009953814988","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":2000,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.397126Z"},"links":{"citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:31dd23454b4669db884158bf7c07418631c8451638dfe1ddc6c8799c7afb4fa8","observation_id":"fc4dd157-e820-40bf-be79-be37d56ceae9","resolution":{"observed_at":"2026-08-12T18:38:42.397126Z","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":"2026-08-12T18:38:43.964887Z","title":null,"venue":null,"work_id":"02cc0a54-86cc-450d-8196-85f1d80bfadd","year":null},"citing_paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-12T18:38:42.432111Z"},"links":{"citing_paper":"/paper/2411.11375"},"observation_digest":"sha256:69a6248b7888a5ed770455e6f27dfe20582fc3088ee6ee33eb0770c9d93d7cd8","observation_id":"49856fd0-f675-4202-af74-314524a67d8f","resolution":{"observed_at":"2026-08-12T18:38:43.968260Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2411.11375","last_updated":"2024-11-18T08:39:24Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-14T21:16:00.414315Z","submitted_at":"2024-11-18T08:39:24Z","title":"Graph Neural Networks on Graph Databases"},"reference_resolution":{"displayed":55,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":18,"verified_exact":6,"verified_fuzzy":31},"total_outbound_references":55},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 0 inbound Pith citation observations for arXiv:2411.11375."}