{"as_of":"2026-08-21T14:38:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c3b22e66c7cfd5ecfe8f405ed316af99b112b23108bfdab1b19942fc097bf2a0","coverage":[{"denominator":41,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":41,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T06:06:24.174284Z","state":"measured"},{"denominator":41,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":41,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+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/1909.00155/citation-record","integrity":"/paper/1909.00155/integrity","json":"/paper/1909.00155/citation-record.json","paper":"/paper/1909.00155"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T06:06:25.287921Z","title":"Imagenet classifi- cation with deep convolutional neural networks,","venue":null,"work_id":"24f8ea5b-cf14-4466-a9df-e104c4e4679e","year":2017},"citing_paper":{"arxiv_id":"1909.00155","last_updated":"2020-04-07T11:34:10Z","snapshot_observed_at":"2026-08-16T11:27:08.334670Z","submitted_at":"2019-08-31T07:12:59Z","title":"EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:23.909212Z"},"links":{"citing_paper":"/paper/1909.00155"},"observation_digest":"sha256:f15cacb2c8013453b3e9eeddc9dfb5d79f5874d0605f14169d192c618dceed3a","observation_id":"4a51f7f6-f4c7-4045-9a6a-771b2ca6b205","resolution":{"observed_at":"2026-08-14T06:06:25.296419Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1609.02907","last_updated":"2017-02-22T09:55:36Z","snapshot_observed_at":"2026-08-17T10:49:36.026134Z","submitted_at":"2016-09-09T19:48:41Z","title":"Semi-Supervised Classification with Graph Convolutional Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1609.02907","snapshot_observed_at":"2026-08-14T06:06:23.916112Z","title":"Semi-supervised classification with graph convolutional networks,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1909.00155","last_updated":"2020-04-07T11:34:10Z","snapshot_observed_at":"2026-08-16T11:27:08.334670Z","submitted_at":"2019-08-31T07:12:59Z","title":"EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks","version":3},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:23.916112Z"},"links":{"cited_paper":"/paper/1609.02907","citing_paper":"/paper/1909.00155"},"observation_digest":"sha256:8ae504d77c81f9c94948234cfdd221aa6c706c553dfad770f5f4ea937c447d6e","observation_id":"52fadbef-bb37-4ed4-aedd-666d59bae939","resolution":{"observed_at":"2026-08-14T06:06:23.916112Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"2026-08-14T06:06:23.923956Z","title":"Fastgcn: Fast learning with graph convolutional networks via importance sampling,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1909.00155","last_updated":"2020-04-07T11:34:10Z","snapshot_observed_at":"2026-08-16T11:27:08.334670Z","submitted_at":"2019-08-31T07:12:59Z","title":"EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks","version":3},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:23.923956Z"},"links":{"cited_paper":"/paper/1801.10247","citing_paper":"/paper/1909.00155"},"observation_digest":"sha256:73881904538a1935353b270bf9d035d1b3fed9d97107b45cb9a37bacba18ce7a","observation_id":"70d787cc-29c2-4ab1-8061-2a7043ade6a2","resolution":{"observed_at":"2026-08-14T06:06:23.923956Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1706.05674","last_updated":"2017-06-20T01:33:33Z","snapshot_observed_at":"2026-08-17T00:03:51.020826Z","submitted_at":"2017-06-18T15:53:59Z","title":"Knowledge Transfer for Out-of-Knowledge-Base Entities: A Graph Neural Network Approach","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.05674","snapshot_observed_at":"2026-08-14T06:06:23.932024Z","title":"Knowl- edge transfer for out-of-knowledge-base entities: A graph neural network approach,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1909.00155","last_updated":"2020-04-07T11:34:10Z","snapshot_observed_at":"2026-08-16T11:27:08.334670Z","submitted_at":"2019-08-31T07:12:59Z","title":"EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks","version":3},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:23.932024Z"},"links":{"cited_paper":"/paper/1706.05674","citing_paper":"/paper/1909.00155"},"observation_digest":"sha256:e2baa4c1e5f169690ed17509fb9d080d51433aab4aa91463705c8784e5901377","observation_id":"90a3f0bd-7955-443b-a096-c43619fae0c9","resolution":{"observed_at":"2026-08-14T06:06:23.932024Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1902.08730","last_updated":"2019-02-23T03:45:31Z","snapshot_observed_at":"2026-08-14T17:12:25.829086Z","submitted_at":"2019-02-23T03:45:31Z","title":"AliGraph: A Comprehensive Graph Neural Network Platform","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.08730","snapshot_observed_at":"2026-08-14T06:06:23.942490Z","title":"Aligraph: A comprehensive graph neural network platform,","venue":null,"work_id":null,"year":1902},"citing_paper":{"arxiv_id":"1909.00155","last_updated":"2020-04-07T11:34:10Z","snapshot_observed_at":"2026-08-16T11:27:08.334670Z","submitted_at":"2019-08-31T07:12:59Z","title":"EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks","version":3},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:23.942490Z"},"links":{"cited_paper":"/paper/1902.08730","citing_paper":"/paper/1909.00155"},"observation_digest":"sha256:4314230752de359a5b2c88c404aa4b7fe560a5ece281bc69c41ace1696195375","observation_id":"263d2e30-0a0c-41f7-89de-4953275c2ebc","resolution":{"observed_at":"2026-08-14T06:06:23.942490Z","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":"2026-08-14T06:06:25.261385Z","title":"Euler: A distributed graph deep learning framework","venue":null,"work_id":"0ce2e9d2-66f5-40a6-b4a0-b24483e004dc","year":null},"citing_paper":{"arxiv_id":"1909.00155","last_updated":"2020-04-07T11:34:10Z","snapshot_observed_at":"2026-08-16T11:27:08.334670Z","submitted_at":"2019-08-31T07:12:59Z","title":"EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks","version":3},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:23.950341Z"},"links":{"citing_paper":"/paper/1909.00155"},"observation_digest":"sha256:efc8bbd2a7611c3fdc756a75b127ea76fa1ee987dac53d99f4a4f34197b9fb2e","observation_id":"f3670fad-4df8-4be5-ab5a-00b52df106cb","resolution":{"observed_at":"2026-08-14T06:06:25.268728Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1909.01315","last_updated":"2020-08-25T15:46:13Z","snapshot_observed_at":"2026-08-18T10:54:34.717319Z","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":"2026-08-14T06:06:23.960869Z","title":"Deep graph library: Towards efficient and scalable deep learning on graphs,","venue":null,"work_id":null,"year":1909},"citing_paper":{"arxiv_id":"1909.00155","last_updated":"2020-04-07T11:34:10Z","snapshot_observed_at":"2026-08-16T11:27:08.334670Z","submitted_at":"2019-08-31T07:12:59Z","title":"EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:23.960869Z"},"links":{"cited_paper":"/paper/1909.01315","citing_paper":"/paper/1909.00155"},"observation_digest":"sha256:ab4f9e992777a5ab319228ed23d74c4fdfbeac921ca53bca578e122f02076c0a","observation_id":"5edffa9f-1767-4a22-a5ef-aae0350df20a","resolution":{"observed_at":"2026-08-14T06:06:23.960869Z","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":"2026-08-14T06:06:23.968305Z","title":"Fast graph representation learning with pytorch geometric,","venue":null,"work_id":null,"year":1903},"citing_paper":{"arxiv_id":"1909.00155","last_updated":"2020-04-07T11:34:10Z","snapshot_observed_at":"2026-08-16T11:27:08.334670Z","submitted_at":"2019-08-31T07:12:59Z","title":"EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks","version":3},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:23.968305Z"},"links":{"cited_paper":"/paper/1903.02428","citing_paper":"/paper/1909.00155"},"observation_digest":"sha256:61ea0c7c0695b4ac801566a1af25fd2c9729ccfe04fb7946bae60927df69d186","observation_id":"118406d5-e764-4002-8f15-c9e41c6936c0","resolution":{"observed_at":"2026-08-14T06:06:23.968305Z","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":"2026-08-14T06:06:25.237427Z","title":"Neugraph: Parallel deep neural network computation on large graphs,","venue":null,"work_id":"7f93d297-c0c3-447c-8861-58e21f43f89e","year":2019},"citing_paper":{"arxiv_id":"1909.00155","last_updated":"2020-04-07T11:34:10Z","snapshot_observed_at":"2026-08-16T11:27:08.334670Z","submitted_at":"2019-08-31T07:12:59Z","title":"EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks","version":3},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:23.975207Z"},"links":{"citing_paper":"/paper/1909.00155"},"observation_digest":"sha256:a3f9577eff89e2fe1699933b41ed883cd82f1ccb56e818d9e72f1a965ad32cd6","observation_id":"94e2f485-afa5-44f9-9ca3-f9d69aa096d9","resolution":{"observed_at":"2026-08-14T06:06:25.243361Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T06:06:25.218113Z","title":"Graph structure in the web,","venue":null,"work_id":"b471fc0c-129d-4bd1-89ca-ab0db076f360","year":2000},"citing_paper":{"arxiv_id":"1909.00155","last_updated":"2020-04-07T11:34:10Z","snapshot_observed_at":"2026-08-16T11:27:08.334670Z","submitted_at":"2019-08-31T07:12:59Z","title":"EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks","version":3},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:23.981429Z"},"links":{"citing_paper":"/paper/1909.00155"},"observation_digest":"sha256:d6572aecc4509c383e77082f3e7986bcf9f0d583fa879626397957be021edf97","observation_id":"0302dd2d-bd7a-4dcb-9b74-46740cee2ad9","resolution":{"observed_at":"2026-08-14T06:06:25.223901Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T06:06:25.198699Z","title":"Collective classification in network data,","venue":null,"work_id":"03936b90-8228-49f1-ac0a-fff132c5cc5d","year":2008},"citing_paper":{"arxiv_id":"1909.00155","last_updated":"2020-04-07T11:34:10Z","snapshot_observed_at":"2026-08-16T11:27:08.334670Z","submitted_at":"2019-08-31T07:12:59Z","title":"EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks","version":3},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:23.988452Z"},"links":{"citing_paper":"/paper/1909.00155"},"observation_digest":"sha256:a05b27286d3d3299e24947d2849dfc09a01347d43f3fbf376b7b1951b4154421","observation_id":"eee78c6e-16e1-4fdd-8194-9d75be325ef5","resolution":{"observed_at":"2026-08-14T06:06:25.205370Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.08434","last_updated":"2021-10-06T12:26:15Z","snapshot_observed_at":"2026-08-19T18:31:02.467365Z","submitted_at":"2018-12-20T09:30:12Z","title":"Graph Neural Networks: A Review of Methods and Applications","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.08434","snapshot_observed_at":"2026-08-14T06:06:23.993964Z","title":"Graph neural networks: A review of methods and applications,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1909.00155","last_updated":"2020-04-07T11:34:10Z","snapshot_observed_at":"2026-08-16T11:27:08.334670Z","submitted_at":"2019-08-31T07:12:59Z","title":"EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks","version":3},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:23.993964Z"},"links":{"cited_paper":"/paper/1812.08434","citing_paper":"/paper/1909.00155"},"observation_digest":"sha256:df4ea595f45cbde0a4222749fe6407f345304b4d36d700c73733c8c2b246e9f2","observation_id":"350577b5-2f02-4bd7-9465-0195c727e608","resolution":{"observed_at":"2026-08-14T06:06:23.993964Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1901.00596","last_updated":"2019-12-04T01:43:00Z","snapshot_observed_at":"2026-08-19T20:25:04.028693Z","submitted_at":"2019-01-03T03:20:55Z","title":"A Comprehensive Survey on Graph Neural Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1901.00596","snapshot_observed_at":"2026-08-14T06:06:23.999551Z","title":"A comprehensive survey on graph neural networks,","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"1909.00155","last_updated":"2020-04-07T11:34:10Z","snapshot_observed_at":"2026-08-16T11:27:08.334670Z","submitted_at":"2019-08-31T07:12:59Z","title":"EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks","version":3},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:23.999551Z"},"links":{"cited_paper":"/paper/1901.00596","citing_paper":"/paper/1909.00155"},"observation_digest":"sha256:8fe01b015ec63292646b478b4ebefe8e0e171ef11a165e873c8c6886595ffdc4","observation_id":"8ce2dc69-9a6f-4421-ac14-4da56083eeb8","resolution":{"observed_at":"2026-08-14T06:06:23.999551Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1704.01212","last_updated":"2017-06-12T20:52:56Z","snapshot_observed_at":"2026-08-16T04:04:33.340455Z","submitted_at":"2017-04-04T23:00:44Z","title":"Neural Message Passing for Quantum Chemistry","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1704.01212","snapshot_observed_at":"2026-08-14T06:06:24.006421Z","title":"Neural message passing for quantum chemistry,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1909.00155","last_updated":"2020-04-07T11:34:10Z","snapshot_observed_at":"2026-08-16T11:27:08.334670Z","submitted_at":"2019-08-31T07:12:59Z","title":"EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks","version":3},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:24.006421Z"},"links":{"cited_paper":"/paper/1704.01212","citing_paper":"/paper/1909.00155"},"observation_digest":"sha256:0d3e647726c378517a128b3b6a430f907895a90c8e7f3cce3c96ec25b4034444","observation_id":"ad4ce0d6-0b9a-4e39-be53-4972a3678ed3","resolution":{"observed_at":"2026-08-14T06:06:24.006421Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1706.02216","last_updated":"2018-09-10T14:26:58Z","snapshot_observed_at":"2026-08-16T10:46:47.387624Z","submitted_at":"2017-06-07T14:51:05Z","title":"Inductive Representation Learning on Large Graphs","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.02216","snapshot_observed_at":"2026-08-14T06:06:24.012683Z","title":"Inductive representa- tion learning on large graphs,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1909.00155","last_updated":"2020-04-07T11:34:10Z","snapshot_observed_at":"2026-08-16T11:27:08.334670Z","submitted_at":"2019-08-31T07:12:59Z","title":"EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks","version":3},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:24.012683Z"},"links":{"cited_paper":"/paper/1706.02216","citing_paper":"/paper/1909.00155"},"observation_digest":"sha256:aa32d54e2ac858ba03b96912fe324204e61301120122bdf4e29efb114ae367ce","observation_id":"13c088d2-2a15-4957-aa39-c96d894e5efe","resolution":{"observed_at":"2026-08-14T06:06:24.012683Z","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":"2026-08-14T06:06:25.171966Z","title":"Modeling relational data with graph convolutional networks,","venue":null,"work_id":"ac289563-b270-4a80-80bb-c88a27c0d6c7","year":2018},"citing_paper":{"arxiv_id":"1909.00155","last_updated":"2020-04-07T11:34:10Z","snapshot_observed_at":"2026-08-16T11:27:08.334670Z","submitted_at":"2019-08-31T07:12:59Z","title":"EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:24.019193Z"},"links":{"citing_paper":"/paper/1909.00155"},"observation_digest":"sha256:7c35a5cb7b85d6d55b066986f8e65303004f7dee28ca43df38289e06c3f03978","observation_id":"128cc391-60c7-4ca1-97c9-1985c6253fd5","resolution":{"observed_at":"2026-08-14T06:06:25.183292Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1612.08083","last_updated":"2017-09-08T22:26:49Z","snapshot_observed_at":"2026-08-15T14:14:24.094805Z","submitted_at":"2016-12-23T20:32:33Z","title":"Language Modeling with Gated Convolutional Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1612.08083","snapshot_observed_at":"2026-08-14T06:06:24.025151Z","title":"Lan- guage modeling with gated convolutional networks,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1909.00155","last_updated":"2020-04-07T11:34:10Z","snapshot_observed_at":"2026-08-16T11:27:08.334670Z","submitted_at":"2019-08-31T07:12:59Z","title":"EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks","version":3},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:24.025151Z"},"links":{"cited_paper":"/paper/1612.08083","citing_paper":"/paper/1909.00155"},"observation_digest":"sha256:73d3f7689ee05f39c2b46174a879df448baceee3270a87f3babad09997b8da1b","observation_id":"2149f482-04f9-4269-98a5-db2c367a31ad","resolution":{"observed_at":"2026-08-14T06:06:24.025151Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1801.01078","last_updated":"2018-02-22T19:28:28Z","snapshot_observed_at":"2026-08-16T22:48:09.572017Z","submitted_at":"2017-12-29T00:57:22Z","title":"Recent Advances in Recurrent Neural Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.01078","snapshot_observed_at":"2026-08-14T06:06:24.030919Z","title":"Recent advances in recurrent neural networks,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1909.00155","last_updated":"2020-04-07T11:34:10Z","snapshot_observed_at":"2026-08-16T11:27:08.334670Z","submitted_at":"2019-08-31T07:12:59Z","title":"EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks","version":3},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:24.030919Z"},"links":{"cited_paper":"/paper/1801.01078","citing_paper":"/paper/1909.00155"},"observation_digest":"sha256:c46283f664336e964914662aa8de94e88b12e7de94d54dbff9688da3d802c85f","observation_id":"690205f5-31af-4cb4-bf52-c04916053cb1","resolution":{"observed_at":"2026-08-14T06:06:24.030919Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1511.05493","last_updated":"2017-09-22T21:36:00Z","snapshot_observed_at":"2026-08-18T19:35:22.009192Z","submitted_at":"2015-11-17T18:10:12Z","title":"Gated Graph Sequence Neural Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1511.05493","snapshot_observed_at":"2026-08-14T06:06:24.037962Z","title":"Gated graph sequence neural networks,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1909.00155","last_updated":"2020-04-07T11:34:10Z","snapshot_observed_at":"2026-08-16T11:27:08.334670Z","submitted_at":"2019-08-31T07:12:59Z","title":"EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks","version":3},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:24.037962Z"},"links":{"cited_paper":"/paper/1511.05493","citing_paper":"/paper/1909.00155"},"observation_digest":"sha256:856568f4dc2c49bb24f28cc1a00fdd5ecb407f5de93eb3786bdbb5c1ccfd55ff","observation_id":"bf868e25-00ef-4541-bc36-203d5a5365c8","resolution":{"observed_at":"2026-08-14T06:06:24.037962Z","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":"2026-08-14T06:06:25.150923Z","title":"Accelerating graph analytics on cpu-fpga heterogeneous platform,","venue":null,"work_id":"350d7477-ab1a-46d9-b92c-bb05b56c5a33","year":2017},"citing_paper":{"arxiv_id":"1909.00155","last_updated":"2020-04-07T11:34:10Z","snapshot_observed_at":"2026-08-16T11:27:08.334670Z","submitted_at":"2019-08-31T07:12:59Z","title":"EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks","version":3},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:24.048424Z"},"links":{"citing_paper":"/paper/1909.00155"},"observation_digest":"sha256:7ce08c751cf8282113de3722a0247429663931ccbdcc31bbdf348f87040912cd","observation_id":"f8978060-c02e-452d-bf0a-1f1c905a6b0f","resolution":{"observed_at":"2026-08-14T06:06:25.157387Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T06:06:25.130111Z","title":"Poster: Domain-specialized cache management for graph analytics,","venue":null,"work_id":"a5fb0e70-a4c6-4cc9-af48-70d4716ad6e7","year":2019},"citing_paper":{"arxiv_id":"1909.00155","last_updated":"2020-04-07T11:34:10Z","snapshot_observed_at":"2026-08-16T11:27:08.334670Z","submitted_at":"2019-08-31T07:12:59Z","title":"EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks","version":3},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:24.055894Z"},"links":{"citing_paper":"/paper/1909.00155"},"observation_digest":"sha256:4ca2a0b5b4ec58fa4867d4ba2be8fc68f609d5f45167ded27193c2c363144512","observation_id":"ab804e4c-52ed-4e72-b37e-8b896586bc54","resolution":{"observed_at":"2026-08-14T06:06:25.136533Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2001.02514","last_updated":"2020-01-07T03:44:46Z","snapshot_observed_at":"2026-07-06T08:49:13.684010Z","submitted_at":"2020-01-07T03:44:46Z","title":"HyGCN: A GCN Accelerator with Hybrid Architecture","version":1},"cited_work":{"arxiv_id":"2001.02514","doi":null,"metadata_source":"pith","pith_arxiv_id":"2001.02514","snapshot_observed_at":"2026-08-14T06:06:24.337076Z","title":"HyGCN: A GCN Accelerator with Hybrid Architecture","venue":"cs.DC","work_id":"2ecf96e7-e74f-44a8-b4cf-264024d9456d","year":2020},"citing_paper":{"arxiv_id":"1909.00155","last_updated":"2020-04-07T11:34:10Z","snapshot_observed_at":"2026-08-16T11:27:08.334670Z","submitted_at":"2019-08-31T07:12:59Z","title":"EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks","version":3},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:24.062113Z"},"links":{"cited_paper":"/paper/2001.02514","citing_paper":"/paper/1909.00155"},"observation_digest":"sha256:e67dc633b751dd0a4643e6513213b42799b18e6f70dbc995e3a3453b116069bc","observation_id":"9de6be86-e53b-4690-9011-307755403b32","resolution":{"observed_at":"2026-08-14T06:06:24.347216Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T06:06:25.107605Z","title":"Characterizing and understanding gcns on gpu,","venue":null,"work_id":"0279ff24-2a85-453f-aa7c-52873aa4056e","year":2020},"citing_paper":{"arxiv_id":"1909.00155","last_updated":"2020-04-07T11:34:10Z","snapshot_observed_at":"2026-08-16T11:27:08.334670Z","submitted_at":"2019-08-31T07:12:59Z","title":"EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks","version":3},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:24.067649Z"},"links":{"citing_paper":"/paper/1909.00155"},"observation_digest":"sha256:cc2ddeb36b02e081d74f6606ce5b691ab8fc09493bf0e02eb5f1c45eff8e3daa","observation_id":"a7d8a6e1-89dd-4300-a42d-390374d46aab","resolution":{"observed_at":"2026-08-14T06:06:25.115444Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1901.01544","last_updated":"2019-01-06T13:52:07Z","snapshot_observed_at":"2026-08-19T13:21:11.840453Z","submitted_at":"2019-01-06T13:52:07Z","title":"Bandwidth Reduction using Importance Weighted Pruning on Ring AllReduce","version":1},"cited_work":{"arxiv_id":"1901.01544","doi":null,"metadata_source":"pith","pith_arxiv_id":"1901.01544","snapshot_observed_at":"2026-08-14T06:06:24.307389Z","title":"Bandwidth Reduction using Importance Weighted Pruning on Ring AllReduce","venue":"cs.LG","work_id":"6101181a-0dd1-4096-97e4-d3efd331b1a1","year":2019},"citing_paper":{"arxiv_id":"1909.00155","last_updated":"2020-04-07T11:34:10Z","snapshot_observed_at":"2026-08-16T11:27:08.334670Z","submitted_at":"2019-08-31T07:12:59Z","title":"EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks","version":3},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:24.073572Z"},"links":{"cited_paper":"/paper/1901.01544","citing_paper":"/paper/1909.00155"},"observation_digest":"sha256:1e9834cea830a1a8d909918226f47571bcd8a54c405ce24a1ee0dffb6b647005","observation_id":"97e986da-bc4c-4f66-adcd-8899ba9c6d44","resolution":{"observed_at":"2026-08-14T06:06:24.315910Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T06:06:25.062187Z","title":"Gridgraph: Large-scale graph processing on a single machine using 2-level hierarchical parti- tioning,","venue":null,"work_id":"778f3e79-bc55-4aba-80be-87a7c3b4804a","year":2015},"citing_paper":{"arxiv_id":"1909.00155","last_updated":"2020-04-07T11:34:10Z","snapshot_observed_at":"2026-08-16T11:27:08.334670Z","submitted_at":"2019-08-31T07:12:59Z","title":"EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks","version":3},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:24.079572Z"},"links":{"citing_paper":"/paper/1909.00155"},"observation_digest":"sha256:f31f36c184235d346498714c7b1b066c420541b02fcf22d712f5fc17b3fed1a1","observation_id":"79b570fa-6371-46ef-b512-0936deae3991","resolution":{"observed_at":"2026-08-14T06:06:25.091663Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T06:06:25.042219Z","title":"Toward an architecture for never-ending language learning,","venue":null,"work_id":"2eb2a53f-cee2-45a2-9f90-70a43b1bca48","year":null},"citing_paper":{"arxiv_id":"1909.00155","last_updated":"2020-04-07T11:34:10Z","snapshot_observed_at":"2026-08-16T11:27:08.334670Z","submitted_at":"2019-08-31T07:12:59Z","title":"EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks","version":3},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:24.085106Z"},"links":{"citing_paper":"/paper/1909.00155"},"observation_digest":"sha256:3aa2f4aefb726d49dc0e2f963271b0edd61d9cc0337eabf9ef8d56646d74800b","observation_id":"b7712cd6-5191-4c7e-bb4c-c162aac857bf","resolution":{"observed_at":"2026-08-14T06:06:25.048727Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1707.03815","last_updated":"2018-02-27T10:20:09Z","snapshot_observed_at":"2026-08-14T20:48:03.238982Z","submitted_at":"2017-07-12T17:54:04Z","title":"Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.03815","snapshot_observed_at":"2026-08-14T06:06:24.090952Z","title":"Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1909.00155","last_updated":"2020-04-07T11:34:10Z","snapshot_observed_at":"2026-08-16T11:27:08.334670Z","submitted_at":"2019-08-31T07:12:59Z","title":"EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks","version":3},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:24.090952Z"},"links":{"cited_paper":"/paper/1707.03815","citing_paper":"/paper/1909.00155"},"observation_digest":"sha256:77e03136e64a10a1186c7b94a0f2670803be2567b66d3784cbed6c6b56de922e","observation_id":"babef922-fc4e-4491-be01-6da8d5f1647d","resolution":{"observed_at":"2026-08-14T06:06:24.090952Z","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":"2026-08-14T06:06:25.021728Z","title":"R-mat: A recursive model for graph mining,","venue":null,"work_id":"f4ee8e2f-4a53-4c44-a444-f8a41c132757","year":2004},"citing_paper":{"arxiv_id":"1909.00155","last_updated":"2020-04-07T11:34:10Z","snapshot_observed_at":"2026-08-16T11:27:08.334670Z","submitted_at":"2019-08-31T07:12:59Z","title":"EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks","version":3},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:24.097235Z"},"links":{"citing_paper":"/paper/1909.00155"},"observation_digest":"sha256:60abe5900c99a127bbdb403470d206e44e74b2069d41fd85dad62b790da7dc27","observation_id":"96ec5102-ee5d-4355-9eed-8db941006b0a","resolution":{"observed_at":"2026-08-14T06:06:25.028804Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T06:06:24.998649Z","title":"Ramulator: A fast and extensible dram simulator,","venue":null,"work_id":"f0013b00-d7b0-48db-aaad-a25caff85b24","year":2016},"citing_paper":{"arxiv_id":"1909.00155","last_updated":"2020-04-07T11:34:10Z","snapshot_observed_at":"2026-08-16T11:27:08.334670Z","submitted_at":"2019-08-31T07:12:59Z","title":"EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks","version":3},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:24.103601Z"},"links":{"citing_paper":"/paper/1909.00155"},"observation_digest":"sha256:0f7e0434922fbef86cb84df0e3c4ad181221dfe10c8051b4101fa9c2be46a4c9","observation_id":"f01e4c79-a2bf-412c-86e7-3efd231f4f27","resolution":{"observed_at":"2026-08-14T06:06:25.006723Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T06:06:24.956135Z","title":"Fine-grained dram: Energy-efficient dram for extreme bandwidth systems,","venue":null,"work_id":"905695c3-d7cc-459c-a785-4af790ad8547","year":2017},"citing_paper":{"arxiv_id":"1909.00155","last_updated":"2020-04-07T11:34:10Z","snapshot_observed_at":"2026-08-16T11:27:08.334670Z","submitted_at":"2019-08-31T07:12:59Z","title":"EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks","version":3},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:24.109407Z"},"links":{"citing_paper":"/paper/1909.00155"},"observation_digest":"sha256:6f190925fc9c20ddf926b305d9c009c5a8e5e6526fe04254b454b9e88f570cec","observation_id":"041c2db9-f821-4667-a1ae-5cd923002673","resolution":{"observed_at":"2026-08-14T06:06:24.964766Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1802.08773","last_updated":"2018-06-23T15:22:19Z","snapshot_observed_at":"2026-08-14T19:42:41.603778Z","submitted_at":"2018-02-24T00:39:09Z","title":"GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.08773","snapshot_observed_at":"2026-08-14T06:06:24.116145Z","title":"Graphrnn: A deep generative model for graphs,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1909.00155","last_updated":"2020-04-07T11:34:10Z","snapshot_observed_at":"2026-08-16T11:27:08.334670Z","submitted_at":"2019-08-31T07:12:59Z","title":"EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks","version":3},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:24.116145Z"},"links":{"cited_paper":"/paper/1802.08773","citing_paper":"/paper/1909.00155"},"observation_digest":"sha256:a508e766ccd0ec25dd9fa8f62e60bf21fe4ac9bfbe2d008916998b735e877fa4","observation_id":"b7a7f7a0-dde6-4829-b0ab-0c6050aae078","resolution":{"observed_at":"2026-08-14T06:06:24.116145Z","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":"2026-08-14T06:06:24.926223Z","title":"Pregel: A system for large-scale graph processing,","venue":null,"work_id":"a7075b6d-6bdb-4a43-9e02-f3d8f4923ccf","year":2010},"citing_paper":{"arxiv_id":"1909.00155","last_updated":"2020-04-07T11:34:10Z","snapshot_observed_at":"2026-08-16T11:27:08.334670Z","submitted_at":"2019-08-31T07:12:59Z","title":"EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks","version":3},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:24.121775Z"},"links":{"citing_paper":"/paper/1909.00155"},"observation_digest":"sha256:15c8b3aa90c0215719818bab4652440b785fefda1177989f55bad2dd209a4f0d","observation_id":"6145adbe-7c9b-40da-acc7-067fe6181719","resolution":{"observed_at":"2026-08-14T06:06:24.938571Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T06:06:24.889248Z","title":"Numa-aware graph-structured analytics,","venue":null,"work_id":"300eec53-6e52-42bd-8c2c-4a048d7e2b70","year":2015},"citing_paper":{"arxiv_id":"1909.00155","last_updated":"2020-04-07T11:34:10Z","snapshot_observed_at":"2026-08-16T11:27:08.334670Z","submitted_at":"2019-08-31T07:12:59Z","title":"EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks","version":3},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:24.127692Z"},"links":{"citing_paper":"/paper/1909.00155"},"observation_digest":"sha256:108c357e795a31b691d376640eb7d24a8a5cd05410d69903a2a485fabd2f52fd","observation_id":"eec22b3f-4e75-477f-a31c-c1205c78590d","resolution":{"observed_at":"2026-08-14T06:06:24.899796Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T06:06:24.863912Z","title":"Powergraph: Distributed graph-parallel computation on natural graphs,","venue":null,"work_id":"caf2c122-f212-4415-a151-84cace310b82","year":2012},"citing_paper":{"arxiv_id":"1909.00155","last_updated":"2020-04-07T11:34:10Z","snapshot_observed_at":"2026-08-16T11:27:08.334670Z","submitted_at":"2019-08-31T07:12:59Z","title":"EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks","version":3},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:24.133176Z"},"links":{"citing_paper":"/paper/1909.00155"},"observation_digest":"sha256:b336e977602943ae22bf56d87c0149640fd120904298c945be6ceede0ccf2a05","observation_id":"cf53419d-8c71-44cb-86ad-4d3852276e65","resolution":{"observed_at":"2026-08-14T06:06:24.872527Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T06:06:24.842336Z","title":"Gemini: A computation- centric distributed graph processing system,","venue":null,"work_id":"75a0c9fb-f538-4c85-b9bd-3ba10cc085f6","year":2016},"citing_paper":{"arxiv_id":"1909.00155","last_updated":"2020-04-07T11:34:10Z","snapshot_observed_at":"2026-08-16T11:27:08.334670Z","submitted_at":"2019-08-31T07:12:59Z","title":"EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks","version":3},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:24.138590Z"},"links":{"citing_paper":"/paper/1909.00155"},"observation_digest":"sha256:b534e58e4946fb8670666fe9fe114eec9cb74101ff5e7daf89822e657a6e93ef","observation_id":"d5239cc1-a1e3-4d20-b521-22e7c799ceb6","resolution":{"observed_at":"2026-08-14T06:06:24.849117Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T06:06:24.820189Z","title":"Gram: Scaling graph computation to the trillions,","venue":null,"work_id":"fc6311fa-6801-4a35-abd1-abbe446069e8","year":2015},"citing_paper":{"arxiv_id":"1909.00155","last_updated":"2020-04-07T11:34:10Z","snapshot_observed_at":"2026-08-16T11:27:08.334670Z","submitted_at":"2019-08-31T07:12:59Z","title":"EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks","version":3},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:24.144335Z"},"links":{"citing_paper":"/paper/1909.00155"},"observation_digest":"sha256:713c75afa7e934e517fb4bf3777ba3a9f9907001bdc3527d073cec394b18a443","observation_id":"e1f8ba2c-27ac-4ced-bd42-fa7cb3929b63","resolution":{"observed_at":"2026-08-14T06:06:24.826485Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T06:06:24.789887Z","title":"Efficient processing of deep neural networks: A tutorial and survey,","venue":null,"work_id":"77639bc1-3238-47de-80c8-e6dcc8757888","year":2017},"citing_paper":{"arxiv_id":"1909.00155","last_updated":"2020-04-07T11:34:10Z","snapshot_observed_at":"2026-08-16T11:27:08.334670Z","submitted_at":"2019-08-31T07:12:59Z","title":"EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks","version":3},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:24.151550Z"},"links":{"citing_paper":"/paper/1909.00155"},"observation_digest":"sha256:414406098865a81908cf874260df32b9a7505860e30026656d2dfc69e5e59984","observation_id":"5b06a69d-46f2-4d60-9ca0-8dfb090b6db5","resolution":{"observed_at":"2026-08-14T06:06:24.802653Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T06:06:24.767041Z","title":"Diannao: A small-footprint high-throughput accelerator for ubiq- uitous machine-learning,","venue":null,"work_id":"3801a5f3-03f0-40e6-be1a-e32a92379953","year":2014},"citing_paper":{"arxiv_id":"1909.00155","last_updated":"2020-04-07T11:34:10Z","snapshot_observed_at":"2026-08-16T11:27:08.334670Z","submitted_at":"2019-08-31T07:12:59Z","title":"EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks","version":3},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:24.156906Z"},"links":{"citing_paper":"/paper/1909.00155"},"observation_digest":"sha256:785198c9dfc0f0e38683162035e2ab123e3abab7f03b4f3fd701a0274a963dbc","observation_id":"d962210f-246a-4b33-b9e6-45795ffe8688","resolution":{"observed_at":"2026-08-14T06:06:24.773396Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1602.01528","last_updated":"2016-05-03T04:27:02Z","snapshot_observed_at":"2026-08-17T00:08:53.302451Z","submitted_at":"2016-02-04T01:28:28Z","title":"EIE: Efficient Inference Engine on Compressed Deep Neural Network","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1602.01528","snapshot_observed_at":"2026-08-14T06:06:24.162423Z","title":"EIE: efficient inference engine on compressed deep neural network,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1909.00155","last_updated":"2020-04-07T11:34:10Z","snapshot_observed_at":"2026-08-16T11:27:08.334670Z","submitted_at":"2019-08-31T07:12:59Z","title":"EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks","version":3},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:24.162423Z"},"links":{"cited_paper":"/paper/1602.01528","citing_paper":"/paper/1909.00155"},"observation_digest":"sha256:a1d7f8eb8631c529fe3cbe399fbb0336e8d66ea801951f72ffaf2bbf5c006fb3","observation_id":"6116abe5-4427-42d1-b6d8-5dc6c2632a5e","resolution":{"observed_at":"2026-08-14T06:06:24.162423Z","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":"2026-08-14T06:06:24.742503Z","title":"Graphicionado: A high-performance and energy-efficient accel- erator for graph analytics,","venue":null,"work_id":"c0a1a3b6-6bcc-408b-8259-73174b45672e","year":2016},"citing_paper":{"arxiv_id":"1909.00155","last_updated":"2020-04-07T11:34:10Z","snapshot_observed_at":"2026-08-16T11:27:08.334670Z","submitted_at":"2019-08-31T07:12:59Z","title":"EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks","version":3},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:24.168205Z"},"links":{"citing_paper":"/paper/1909.00155"},"observation_digest":"sha256:620ff81a13678192a5b4157308aaa3630dd7625673b0d26796ea9f43d1ace180","observation_id":"012a2493-8f8c-4334-b6f6-4e82c82f6935","resolution":{"observed_at":"2026-08-14T06:06:24.748887Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T06:06:24.721925Z","title":"Alleviating irregularity in graph analytics acceleration: A hardware/software co-design approach,","venue":null,"work_id":"c20a3e86-a8c3-46b6-9570-22011acafd4a","year":2019},"citing_paper":{"arxiv_id":"1909.00155","last_updated":"2020-04-07T11:34:10Z","snapshot_observed_at":"2026-08-16T11:27:08.334670Z","submitted_at":"2019-08-31T07:12:59Z","title":"EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks","version":3},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:24.174284Z"},"links":{"citing_paper":"/paper/1909.00155"},"observation_digest":"sha256:b22fe6d9090017675c6a077a6687dfa18e1fdfc6118dfe1b207b385de875152c","observation_id":"e9195f0c-3c30-471f-a5fd-e7664fdbbb92","resolution":{"observed_at":"2026-08-14T06:06:24.728576Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"1909.00155","last_updated":"2020-04-07T11:34:10Z","latest_version":3,"primary_category":"cs.DC","snapshot_observed_at":"2026-08-16T11:27:08.334670Z","submitted_at":"2019-08-31T07:12:59Z","title":"EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks"},"reference_resolution":{"displayed":41,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":16,"verified_exact":2,"verified_fuzzy":23},"total_outbound_references":41},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:1909.00155."}