{"as_of":"2026-08-14T21:55:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f27f06db92010eadbf906de280db907749f62466cbfc666af439b036189a53a9","coverage":[{"denominator":73,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":73,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T17:52:48.537188Z","state":"measured"},{"denominator":73,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":73,"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/2501.11880/citation-record","integrity":"/paper/2501.11880/integrity","json":"/paper/2501.11880/citation-record.json","paper":"/paper/2501.11880"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T17:52:49.754544Z","title":"Temporal graph networks for deep learning on dynamic graphs,","venue":null,"work_id":"934057ba-3966-47cb-bad8-0b821f28bfe7","year":2020},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.190118Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:d82f3cd8523f72caf5914bccf3184536e2cbc1bdb52530dd442fdaa0d9670a45","observation_id":"5fde0cf9-7374-4f22-ba8e-3bec6ca32e02","resolution":{"observed_at":"2026-08-10T17:52:49.759432Z","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":"2026-08-10T17:52:49.740063Z","title":"Inductive representation learning on temporal graphs,","venue":null,"work_id":"f93839bb-e391-4885-a58b-45c035cd0500","year":2020},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.195097Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:ddedabb4446607de58bef4389d7f1e5187089fc5ebbd5f2c1d45dc77780cb6a3","observation_id":"6cf4bc9d-a47f-44e9-bb49-bc8aca28dea2","resolution":{"observed_at":"2026-08-10T17:52:49.744841Z","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":"2026-08-10T17:52:49.724793Z","title":"Inductive representation learning in temporal networks via causal anonymous walks,","venue":null,"work_id":"8b3d6865-f2ed-4264-b6b2-c61cf274b6f3","year":2021},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.200035Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:a5d512c2bb4577236b0f9ccce95ccc2ec0a76d79e29498919737f03d30aca64c","observation_id":"9affe3ec-2ab5-41a2-8b3b-afbc22ea9600","resolution":{"observed_at":"2026-08-10T17:52:49.729599Z","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":"2026-08-10T17:52:49.709557Z","title":"Prov- ably expressive temporal graph networks,","venue":null,"work_id":"7b572b8b-0a45-4009-ba24-ea85a6541f7d","year":2022},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.205241Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:d986e02ca35f17232f86598a7861f904dc8a1049453940d46ddb8559de978b71","observation_id":"1256a589-e0f4-4eaf-b16b-4db17b0fc1c6","resolution":{"observed_at":"2026-08-10T17:52:49.714429Z","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":"2026-08-10T17:52:49.695717Z","title":"A review of relational machine learning for knowl- edge graphs,","venue":null,"work_id":"68b7d382-40bb-45d4-91b2-d0c080199be4","year":2015},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.210052Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:1c6891c193f9481d3748059e41b00419b3cb20b87e8513e56a9b864572099bc5","observation_id":"a5c009c9-91d1-46d4-b281-19e805b840c5","resolution":{"observed_at":"2026-08-10T17:52:49.700038Z","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":"2001.09061","last_updated":"2020-01-24T15:47:12Z","snapshot_observed_at":"2026-08-13T10:53:34.744046Z","submitted_at":"2020-01-24T15:47:12Z","title":"Kernel of CycleGAN as a Principle homogeneous space","version":1},"cited_work":{"arxiv_id":"2001.09061","doi":null,"metadata_source":"pith","pith_arxiv_id":"2001.09061","snapshot_observed_at":"2026-08-10T17:52:48.824252Z","title":"Kernel of CycleGAN as a Principle homogeneous space","venue":"cs.LG","work_id":"77ce557a-509c-4b8b-9a12-67914b946d3a","year":2020},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.214870Z"},"links":{"cited_paper":"/paper/2001.09061","citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:60e5d17318cba8024743b1bf2e9e1a6d3c546757c658dd8e73156ad3bfe6e90e","observation_id":"0b66b394-19c1-4a75-b0fd-bd0f9bea4119","resolution":{"observed_at":"2026-08-10T17:52:48.829078Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"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":"2026-08-10T17:52:49.681028Z","title":"Kgat: Knowledge graph attention network for recommenda- tion,","venue":null,"work_id":"3d0ae617-dd31-4612-aa99-85f44a4cc924","year":2019},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.220589Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:902a9abafb2d1623af1d5eec9967f4939fcea615b7ade31ff8f18bde376ad627","observation_id":"58a06f10-4b84-47a8-83ec-728eb676b827","resolution":{"observed_at":"2026-08-10T17:52:49.685991Z","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":"2026-08-10T17:52:49.665701Z","title":"Graph rep- resentation learning for biological networks,","venue":null,"work_id":"824e7950-f03a-4aa5-abad-c5bc59384156","year":2021},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.225101Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:67ff0f9b4d3c4be287dd5b37697e6da89f53474ed89282aa1e589a61be380c33","observation_id":"51eba098-225d-4943-80ee-ad0b383785be","resolution":{"observed_at":"2026-08-10T17:52:49.670826Z","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":"2026-08-10T17:52:49.649455Z","title":"Pro- tein interface prediction using graph convolutional net- works,","venue":null,"work_id":"c4746045-a2be-401c-91e7-3fa7c52cefda","year":2017},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.229822Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:5f91d74367e72b073b2fc0d84d3197bef3e606fa32c30a39095178796c8d94cc","observation_id":"df7f26e5-6bcd-4914-8805-283e83a43a5d","resolution":{"observed_at":"2026-08-10T17:52:49.654327Z","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":"2026-08-10T17:52:49.633859Z","title":"Neighborhood-aware scalable tem- poral network representation learning,","venue":null,"work_id":"c0677d5e-4abe-4f2a-ac3e-1e3d1c0edb25","year":2022},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.234375Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:a261b1c8592d48a42b0fddfe0674d486e12dd09c775b8237f2802ff08e54408d","observation_id":"bfc9cef3-2c5c-4ea2-bee6-6bfe539d121e","resolution":{"observed_at":"2026-08-10T17:52:49.639064Z","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":"2026-08-10T17:52:49.618652Z","title":"Towards better dy- namic graph learning: New architecture and unified li- brary,","venue":null,"work_id":"4ead7572-bdb1-437c-87c3-453bd923b1a3","year":2023},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.238910Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:c12abb85f393744e070d6e7a38a88510f143452ec6329cffadbc207de6042f16","observation_id":"41d60a30-7f85-4116-a6a5-f277bf4f32fc","resolution":{"observed_at":"2026-08-10T17:52:49.623448Z","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":"2026-08-10T17:52:49.603230Z","title":"Representation learning for dynamic graphs: A survey,","venue":null,"work_id":"4d79a523-0eca-4a1e-8775-8b172b1d0705","year":2020},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.243605Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:66f0af3b1751b9d07d7216b6968de13463e4c08ca0c5d9e0449446554946f82e","observation_id":"b661fdac-c134-46db-bd2e-139977d195c6","resolution":{"observed_at":"2026-08-10T17:52:49.608495Z","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":"2304.05729","last_updated":"2023-04-12T09:39:17Z","snapshot_observed_at":"2026-08-13T12:04:24.756520Z","submitted_at":"2023-04-12T09:39:17Z","title":"Dynamic Graph Representation Learning with Neural Networks: A Survey","version":1},"cited_work":{"arxiv_id":"2304.05729","doi":null,"metadata_source":"pith","pith_arxiv_id":"2304.05729","snapshot_observed_at":"2026-08-10T17:52:48.801516Z","title":"Dynamic Graph Representation Learning with Neural Networks: A Survey","venue":"cs.LG","work_id":"e5bd2fa9-403e-4a03-8547-87ccad69a097","year":2023},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.248408Z"},"links":{"cited_paper":"/paper/2304.05729","citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:e4b5b3b4fbd3e8592cde0c45afc227bc52ea2dc176f762769fb5d683c88da32b","observation_id":"f4b02c13-332e-4ffd-a9e7-be29c45670cc","resolution":{"observed_at":"2026-08-10T17:52:48.806829Z","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":{"arxiv_id":"2203.10480","last_updated":"2022-03-27T19:09:39Z","snapshot_observed_at":"2026-08-13T16:19:23.451144Z","submitted_at":"2022-03-20T07:51:46Z","title":"Encoder-Decoder Architecture for Supervised Dynamic Graph Learning: A Survey","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.10480","snapshot_observed_at":"2026-08-10T17:52:48.253533Z","title":"Encoder- decoder architecture for supervised dynamic graph learning: A survey,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.253533Z"},"links":{"cited_paper":"/paper/2203.10480","citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:161fad0e709777fad020f9508ba46af87ab7c71e5c56b33efb693ea3fe2cea8e","observation_id":"9df3e973-4995-4af0-a3bd-f5a96f4b0645","resolution":{"observed_at":"2026-08-10T17:52:48.253533Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.06434","last_updated":"2023-01-16T13:52:25Z","snapshot_observed_at":"2026-08-13T13:03:45.934121Z","submitted_at":"2023-01-16T13:52:25Z","title":"Behavior Trees for Robust Task Level Control in Robotic Applications","version":1},"cited_work":{"arxiv_id":"2301.06434","doi":null,"metadata_source":"pith","pith_arxiv_id":"2301.06434","snapshot_observed_at":"2026-08-10T17:52:48.764355Z","title":"Behavior Trees for Robust Task Level Control in Robotic Applications","venue":"cs.RO","work_id":"ab1e527d-3474-43db-8761-1d907d19aab0","year":2023},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.258658Z"},"links":{"cited_paper":"/paper/2301.06434","citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:af5aceca245a1fce7aaeedf8ebeebf59278c4c810d6fed2817c502e0bf0901e7","observation_id":"e4ec30b7-e72d-48a2-a79f-8871460bca3c","resolution":{"observed_at":"2026-08-10T17:52:48.769431Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"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":"2026-08-10T17:52:49.588444Z","title":"Motif-preserving dynamic attributed network embed- ding,","venue":null,"work_id":"5e331a6d-e2e6-458f-9da4-57eab36b1b69","year":2021},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.263511Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:7b6759c6e50285de06595b776ea75cb57071359e6c6f3760a1f4605ec751fa33","observation_id":"cae7a800-7be2-4b3b-a99f-25b3de3c27f3","resolution":{"observed_at":"2026-08-10T17:52:49.593188Z","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":"2026-08-10T17:52:48.268346Z","title":"Community detection in graphs,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.268346Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:4ec8115c8c1d804641d71d34bf890cfebb3b3a204719251833924a8d5cbc441b","observation_id":"e75faed3-4fc7-4100-aeb2-2702908b27c0","resolution":{"observed_at":"2026-08-10T17:52:48.268346Z","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":"2026-08-10T17:52:48.272777Z","title":"Finding and evaluating community structure in networks,","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.272777Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:3cbf54f7de479351b7a238f255da01460f09878927c1411928b92781ab8e434d","observation_id":"6540bff4-a04e-417d-a1e2-18805f79915a","resolution":{"observed_at":"2026-08-10T17:52:48.272777Z","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":"2026-08-10T17:52:49.554184Z","title":"A multiobjective evolution- ary algorithm based on similarity for community detec- tion from signed social networks,","venue":null,"work_id":"cdc9bada-cbe2-48a9-b635-f63d0d340911","year":2014},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.277289Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:0635f9f1136c33478558ef55e339375b78bfc86d45815c172a8ebedb8b75cc35","observation_id":"124a60c2-88dc-4324-a9fc-cd5655407221","resolution":{"observed_at":"2026-08-10T17:52:49.559296Z","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":"2026-08-10T17:52:49.539472Z","title":"Overlapping community detection in directed and undirected attributed networks using a multiobjective evolutionary algorithm,","venue":null,"work_id":"bd12e951-afc2-4d02-a991-bd3e12eb1bb7","year":2021},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.281868Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:28e624837b8b01aac781e7cf7db8c4f2528c47c32a4e2201c79edd243d8591b4","observation_id":"94a051ba-a52d-49d2-bb50-c559c3d40463","resolution":{"observed_at":"2026-08-10T17:52:49.544318Z","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":"2026-08-10T17:52:49.524512Z","title":"Neural ordinary differential equations,","venue":null,"work_id":"14075432-3e35-4a4a-8665-fe272aae8292","year":2018},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.286304Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:c2f7c3c3181ae84f7e2707e283f77668513d4cb8eee6ca630e25929f090cebc8","observation_id":"7bc0022d-b14c-408a-a92e-4be35f90efa9","resolution":{"observed_at":"2026-08-10T17:52:49.529090Z","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":"2202.08408","last_updated":"2022-11-23T07:18:06Z","snapshot_observed_at":"2026-08-13T16:39:23.696917Z","submitted_at":"2022-02-17T02:17:31Z","title":"Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs","version":2},"cited_work":{"arxiv_id":"2202.08408","doi":null,"metadata_source":"pith","pith_arxiv_id":"2202.08408","snapshot_observed_at":"2026-08-10T17:52:48.742729Z","title":"Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs","venue":"cs.LG","work_id":"ccd6d6be-f8c1-4dfc-92a1-130ace75c039","year":2022},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.290896Z"},"links":{"cited_paper":"/paper/2202.08408","citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:01cce598950d8da49fe8e2fda28f4e2bbd09ab7851e5727351fef7dc197d8c76","observation_id":"52283124-339d-49e7-a1c6-f9ef2dd0dfbf","resolution":{"observed_at":"2026-08-10T17:52:48.748022Z","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":{"arxiv_id":"2006.09773","last_updated":"2021-10-15T00:09:21Z","snapshot_observed_at":"2026-07-06T09:29:58.070206Z","submitted_at":"2020-06-17T10:47:03Z","title":"Neural Ordinary Differential Equation Control of Dynamics on Graphs","version":5},"cited_work":{"arxiv_id":"2006.09773","doi":null,"metadata_source":"pith","pith_arxiv_id":"2006.09773","snapshot_observed_at":"2026-08-10T17:52:48.720972Z","title":"Neural Ordinary Differential Equation Control of Dynamics on Graphs","venue":"cs.LG","work_id":"39895c5a-d9fa-40b5-9c54-84f20ce421a3","year":2020},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.296005Z"},"links":{"cited_paper":"/paper/2006.09773","citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:54af84b0f7978e096e8d117d81ec8ab07df5153015d72d34f35426fd337ccd4d","observation_id":"0978717e-14a0-4755-abb2-abc084702792","resolution":{"observed_at":"2026-08-10T17:52:48.726203Z","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":{"arxiv_id":"2312.11198","last_updated":"2023-12-18T13:45:33Z","snapshot_observed_at":"2026-08-13T04:59:30.484432Z","submitted_at":"2023-12-18T13:45:33Z","title":"Signed Graph Neural Ordinary Differential Equation for Modeling Continuous-time Dynamics","version":1},"cited_work":{"arxiv_id":"2312.11198","doi":null,"metadata_source":"pith","pith_arxiv_id":"2312.11198","snapshot_observed_at":"2026-08-10T17:52:48.696594Z","title":"Signed Graph Neural Ordinary Differential Equation for Modeling Continuous-time Dynamics","venue":"cs.LG","work_id":"b482f0e6-aa50-4df5-8659-8d01536df364","year":2023},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.301046Z"},"links":{"cited_paper":"/paper/2312.11198","citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:b793b6560568f11dba50de8b7094e268823809263ecfc58d6e9112415f008501","observation_id":"7cde5b8e-d117-47e1-961b-d77a3ef6b3c3","resolution":{"observed_at":"2026-08-10T17:52:48.703330Z","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":"2026-08-10T17:52:49.509788Z","title":"Streaming dynamic graph neural net- works for continuous-time temporal graphs,","venue":null,"work_id":"be11582b-ec3c-4b11-8739-601fbdd6ebcd","year":2022},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.305959Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:7b616a634dfb38cefac503549db6a447b3173e44fda9f5d628a2694535d52327","observation_id":"503d2948-9bee-47ef-8193-319746c5518a","resolution":{"observed_at":"2026-08-10T17:52:49.514576Z","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":"2026-08-10T17:52:49.494874Z","title":"Higher-order knowledge transfer for dynamic community detection with great changes,","venue":null,"work_id":"02a5a4ef-5c0b-4b4b-b084-ecfd31a5bd19","year":2024},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.310405Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:49796e3b4c9aae23ddfa32712d2a1904e84a9867851f6476534255b56a7de001","observation_id":"0914cbed-7d85-4863-955f-468a3c9631c8","resolution":{"observed_at":"2026-08-10T17:52:49.499872Z","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":"2111.10447","last_updated":"2023-01-30T04:16:30Z","snapshot_observed_at":"2026-08-14T10:16:26.975216Z","submitted_at":"2021-11-19T21:44:23Z","title":"DyFormer: A Scalable Dynamic Graph Transformer with Provable Benefits on Generalization Ability","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.10447","snapshot_observed_at":"2026-08-10T17:52:48.314557Z","title":"Dynamic graph representa- tion learning via graph transformer networks,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.314557Z"},"links":{"cited_paper":"/paper/2111.10447","citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:6a99380ac6a91c21637cb3a93dce3d77a17792add6c64e8cf286fe953b96fddf","observation_id":"b1ab926b-5bfb-4abe-ab3c-82436b40f15c","resolution":{"observed_at":"2026-08-10T17:52:48.314557Z","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":"2026-08-10T17:52:49.480312Z","title":"dyngraph2vec: Capturing network dynamics using dynamic graph representation learning,","venue":null,"work_id":"bb42256c-16f7-453c-8ff4-cabc7de07ad7","year":2020},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.319512Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:bb865fc3ab7a8b8c1625b988e230126e3599d678fb0796c0ad7af3a1f9c7e948","observation_id":"603b5df7-f2e8-4c09-9e7a-031d8462fb24","resolution":{"observed_at":"2026-08-10T17:52:49.484823Z","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":"2026-08-10T17:52:49.466897Z","title":"Evolvegcn: Evolving graph convolutional networks for dynamic graphs,","venue":null,"work_id":"2dfb6b2f-7c01-44e3-8b43-118d07bf7246","year":2020},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.323854Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:20fd3215b4cde72dfc9789f201306d3b2df51c601ea4e96f8efb6d073459a9cf","observation_id":"f9acdf99-06d3-4342-94dc-fc107351ac94","resolution":{"observed_at":"2026-08-10T17:52:49.471215Z","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":"2026-08-10T17:52:49.452965Z","title":"Dysat: Deep neural representation learning on dy- namic graphs via self-attention networks,","venue":null,"work_id":"1dc65dfc-d889-42e4-bb5f-d7402d81377d","year":2020},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.328130Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:53128c16930ae431e6b188b4142901f365ecb4f3be7b1a8542cbae2618a7a538","observation_id":"0c0098eb-1bf7-4812-9469-135b27c959ed","resolution":{"observed_at":"2026-08-10T17:52:49.457522Z","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":"2026-08-10T17:52:49.438619Z","title":"Roland: graph learning framework for dynamic graphs,","venue":null,"work_id":"13a4484c-6ecb-44cb-ba21-b7a8908b6aef","year":2022},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.332675Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:4ed0a8b816d91f6ef3b2230c3a448d0a0d31e8af3dc390c0a47147d280133f85","observation_id":"68ad6f0e-6cfc-4512-b0a6-10c214f92ac9","resolution":{"observed_at":"2026-08-10T17:52:49.443435Z","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":"2026-08-10T17:52:49.424264Z","title":"Latent ordinary differential equations for irregularly-sampled time series,","venue":null,"work_id":"9edbe560-83ec-4076-8fac-b5785488188e","year":2019},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.336813Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:0cba2e7bd8327626fe863085be8a42474419e9e2d0716741d5c9d691c8a6f96b","observation_id":"62c7268e-d237-44d6-87ea-04d8fb749bc6","resolution":{"observed_at":"2026-08-10T17:52:49.428973Z","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":"2026-08-10T17:52:49.409591Z","title":"Continuous graph neural networks,","venue":null,"work_id":"f9ca2ee5-8a30-4b0d-95b3-165c2d6c046d","year":2020},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.340977Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:836bb7268fdcc9e3886eae02e969c638011ac073694d75e9b1f24c653c24e35f","observation_id":"df45231c-3daf-40f6-83c6-5ee18ea1fd60","resolution":{"observed_at":"2026-08-10T17:52:49.414389Z","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":"2026-08-10T17:52:49.394917Z","title":"The graph neural network model,","venue":null,"work_id":"ae6fd84a-6422-495b-ad80-3bcc9c8c71eb","year":2008},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.345074Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:bdab5d732cae934e429b8ffe181c75e318eb95c1fc09ed0475c2107c8e5bb805","observation_id":"5a42b1dc-9f29-4cf4-83b3-9f969f806432","resolution":{"observed_at":"2026-08-10T17:52:49.399703Z","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":"2026-08-10T17:52:49.380187Z","title":"Autosgnn: Automatic propagation mechanism discovery for spec- tral graph neural networks,","venue":null,"work_id":"9254990f-6077-4356-86fc-01629ad55740","year":2025},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.349596Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:471a9743b763745b35863449d0554fb5690389a0c91737e50f90d4d4e390fd25","observation_id":"d7fb81b7-2f46-4d82-9b75-74a5d3d5af38","resolution":{"observed_at":"2026-08-10T17:52:49.385113Z","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":"2026-08-10T17:52:49.365240Z","title":"Finding structure in time,","venue":null,"work_id":"8d7f32cd-8f20-449c-811a-a0ce0ba5df5a","year":1990},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.354616Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:50c8f3d396a03d346cdcc31c9835b0c314270d37a336a328dff1ce9f6bef12e0","observation_id":"42140969-feea-4ea8-97c8-b4bbea5826ee","resolution":{"observed_at":"2026-08-10T17:52:49.369790Z","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":"2026-08-10T17:52:49.350120Z","title":"Predicting dy- namic embedding trajectory in temporal interaction net- works,","venue":null,"work_id":"32fae421-a724-4629-88d9-b156c4c5d2ed","year":2019},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.359399Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:df279a2a4da24cdc1cad684a02d5ca22e44c186eeb1745fc537c347ee363e58c","observation_id":"bc07a0ef-c107-4bd9-9059-e829485c73bc","resolution":{"observed_at":"2026-08-10T17:52:49.355187Z","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":"2026-08-10T17:52:49.334614Z","title":"Fast algorithm for detecting commu- nity structure in networks,","venue":null,"work_id":"e19487d8-7e36-487b-b03c-3eb61f39ebf9","year":2004},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.364224Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:38b69846e08f7b3128540b8f19191ab246489d3d93be32a7895a7f282c01355f","observation_id":"347fd0d0-80d4-4fde-b6d6-200d27b49ac8","resolution":{"observed_at":"2026-08-10T17:52:49.339799Z","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":"2026-08-10T17:52:49.320129Z","title":"Fast unfolding of communities in large networks,","venue":null,"work_id":"37d0c0ba-56f3-4f5b-9c8f-ecfc30e6b4d0","year":2008},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.369075Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:6c697a116b102defed7dadb5c6e40c78dcc51c9587646a1c5e1e21dcb50109a3","observation_id":"92f5741d-d7e4-414a-a74d-a70a5f14f7cf","resolution":{"observed_at":"2026-08-10T17:52:49.324797Z","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":"2026-08-10T17:52:49.305426Z","title":"Community detection in networks with node attributes,","venue":null,"work_id":"8757426d-6bec-4db5-b37a-37141569b7fd","year":2013},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.373810Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:a75a6315aa8570e6e650b81f2313b58d9162061996469879e5b889f9776db3c8","observation_id":"de0ec933-e56e-4598-9efa-a00231ff4868","resolution":{"observed_at":"2026-08-10T17:52:49.310261Z","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":"2026-08-10T17:52:49.290255Z","title":"Overlapping community de- tection at scale: a nonnegative matrix factorization ap- proach,","venue":null,"work_id":"ed949092-49c5-4ffa-b740-0bfd4bb9d7ae","year":2013},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.378299Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:dd08a43d21c2eb9ba97e47fd91a2f150039123a6c82da5e2bf4b14d2fa5cee9c","observation_id":"4908867b-ba2c-4e1a-ac3f-0ad793437040","resolution":{"observed_at":"2026-08-10T17:52:49.295196Z","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":"2026-08-10T17:52:49.275647Z","title":"Community detection in attributed graphs: An embedding approach,","venue":null,"work_id":"b75b16ff-e037-4fe9-b163-ca950973861b","year":2017},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.382831Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:ca3ed7be695301e429d35517532fc3cec0b7e6638d9e946beb04680e6143aaa3","observation_id":"d5269436-ab5d-4aa4-a008-201655d2ed59","resolution":{"observed_at":"2026-08-10T17:52:49.280153Z","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":"2026-08-10T17:52:49.261769Z","title":"Dyrep: Learning representations over dynamic graphs,","venue":null,"work_id":"f0abc750-113f-41b1-9af2-4ce07692c8e8","year":2019},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.387452Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:e600210d638aa54572a6dd936b13d1c3a08c01a65cfd252530dd5f4c5005ee0f","observation_id":"e2a33b99-4744-476f-81e9-b87bf0d2a58a","resolution":{"observed_at":"2026-08-10T17:52:49.266207Z","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":"2026-08-10T17:52:49.247505Z","title":"Continuous-time dynamic network embed- dings,","venue":null,"work_id":"7050c01c-66e7-42ea-a7ea-72586841e521","year":2018},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.392328Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:02d4a8914ea60b0dc20e5540ace2651fac175904c11b4678c6a48eee8f22128f","observation_id":"6b1e6299-7d72-4ca7-9322-353f8d7796d4","resolution":{"observed_at":"2026-08-10T17:52:49.251937Z","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":"2026-08-10T17:52:49.232844Z","title":"Snap datasets: Stanford large network dataset collection","venue":null,"work_id":"8955f18a-2e9c-4af2-bae7-7bdb5fb43b89","year":2014},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.396814Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:7a05c0537112d6efd19e6d552dcc1dba7c9e82af9ee29d69bfac438f2cff21ba","observation_id":"b71c40e8-5924-4fee-ba75-6a0b4fdc56e2","resolution":{"observed_at":"2026-08-10T17:52:49.237729Z","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":"2026-08-10T17:52:49.217751Z","title":"Learning tree-based deep model for recommender sys- tems,","venue":null,"work_id":"b0d6db61-d40e-421a-81ad-021e45b4db22","year":2018},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.401513Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:5e6e47244fcb8ecc2eea27e8e949a13c803eefa0b0f1b0072e547905b43c4a19","observation_id":"936df405-86a7-41d6-b132-fde3a4101145","resolution":{"observed_at":"2026-08-10T17:52:49.222922Z","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":"2026-08-10T17:52:49.201855Z","title":"Learning phrase representations using rnn encoder-decoder for statistical machine translation,","venue":null,"work_id":"f363f0c9-9937-4c86-9b74-c30912c1db3a","year":2014},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.406406Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:75c42c8d85d8c842358037270f0d618bbe5c01f9c46f1dc29bd53e755f0da59a","observation_id":"01d99693-a7e5-494a-9eb6-bf6071a03bbe","resolution":{"observed_at":"2026-08-10T17:52:49.206740Z","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":"2026-08-10T17:52:49.186599Z","title":"Neural spatio- temporal point processes,","venue":null,"work_id":"49b81f6e-7752-4600-8632-f4da760566e3","year":2020},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.411168Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:3c63b6386c39826bf3fd2443d0b92b991582a7a6d1be2acd90a4873ccb979bc0","observation_id":"151ea905-c172-4433-befa-505e268f889d","resolution":{"observed_at":"2026-08-10T17:52:49.191559Z","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":"2026-08-10T17:52:49.171458Z","title":"Ef- ficient estimation of word representations in vector space,","venue":null,"work_id":"62253d2c-2910-4f8c-8467-5c34b30fc8bf","year":2013},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.415587Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:b17d817991789ebddc012e7bee74fc28a626c27342ece18c34a582f71a5bbbc7","observation_id":"e3da673f-9549-4ee0-888e-5abb02232879","resolution":{"observed_at":"2026-08-10T17:52:49.176228Z","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":"2026-08-10T17:52:49.156421Z","title":"node2vec: Scalable fea- ture learning for networks,","venue":null,"work_id":"081ea3fd-38af-4e27-a56d-198e48381df0","year":2016},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.420376Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:f926a374450e2a42454fbd2a421853113af66d4211773eb16da8471fae053f7d","observation_id":"b0b433c2-051a-4d2f-ad23-e65e7d815f13","resolution":{"observed_at":"2026-08-10T17:52:49.161424Z","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":"2026-08-10T17:52:49.141816Z","title":"DeepWalk: On- line learning of social representations,","venue":null,"work_id":"d98e78a9-1307-4aff-9836-3a0595cac5b2","year":2014},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.424770Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:e1aa272facc8c7450ef80e917153f76aff62beeb56cd847c939cf7923ade30ad","observation_id":"b76f2e40-130f-436e-8674-9e6c2dc6c391","resolution":{"observed_at":"2026-08-10T17:52:49.146639Z","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":"2026-08-10T17:52:49.126624Z","title":"LINE: Large-scale information network embedding,","venue":null,"work_id":"6394056d-f31e-46f1-b832-0456b3294f01","year":2015},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.429496Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:9d917c54a3d6f3ad58bfce4a51ad5f0f083974609594831b83510ce236dd58e8","observation_id":"4e4d45ec-319d-4622-adef-0a24abfa0b42","resolution":{"observed_at":"2026-08-10T17:52:49.131744Z","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":"2026-08-10T17:52:49.111609Z","title":"struc2vec: Learning node representations from struc- tural identity,","venue":null,"work_id":"5811f78b-bfdb-4576-a8c8-aede5f93c684","year":2017},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.434368Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:654b642891993c96fadcda4a9a6120d977aec84b9974d724235765155b95de3d","observation_id":"43155db4-5522-469a-a674-515c50e91e40","resolution":{"observed_at":"2026-08-10T17:52:49.116522Z","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":"2026-08-10T17:52:49.096434Z","title":"Inductive rep- resentation learning on large graphs,","venue":null,"work_id":"988cf31b-41b1-4726-b95e-a51999b758ad","year":2017},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.439154Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:7051e3e400faf3ee563ecd02c6d0f6a0abd31d4fdaae8160bb02f3dde214157c","observation_id":"713b388f-60a3-4f6a-8d27-899051f487c0","resolution":{"observed_at":"2026-08-10T17:52:49.101372Z","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":"2026-08-10T17:52:49.082083Z","title":"Commu- nity preserving network embedding,","venue":null,"work_id":"e90e088b-3034-427e-97ac-e1e30408649b","year":2017},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.443821Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:50600febd0c052e3ca7a8ff64f2f3cc74e89e4b06774a72bc77abbad68ec4023","observation_id":"999b9168-1d34-4b77-85fd-51b4a7aed1fd","resolution":{"observed_at":"2026-08-10T17:52:49.086363Z","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":"2026-08-10T17:52:49.068001Z","title":"The network data repos- itory with interactive graph analytics and visualization,","venue":null,"work_id":"c08ae15f-039b-4cad-b94b-745ce46c3490","year":null},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.448470Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:038fab6df4ed415ee4e7fbe7fa31227649918ca303491425257437d9fc7a30c1","observation_id":"8c379fe6-bef4-4cff-babb-00955679670f","resolution":{"observed_at":"2026-08-10T17:52:49.072458Z","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":"2026-08-10T17:52:49.038648Z","title":"Distributed representations of words and phrases and their compositionality,","venue":null,"work_id":"daaad647-22e4-4672-96b6-50cb26bf8cde","year":2013},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.458757Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:62a9dd082bc3ee31ec11d3b5792770ea868cb3dd3b7166b93988f4dea1b422b5","observation_id":"2474af00-3fd3-4cfb-863e-db7aee264131","resolution":{"observed_at":"2026-08-10T17:52:49.043970Z","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":"2026-08-10T17:52:49.022357Z","title":"Neural word embedding as implicit matrix factorization,","venue":null,"work_id":"43f9449a-0a6f-4d4a-a222-e5c51d2a4e3a","year":2014},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.463661Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:bb936005e9425e961e6b77af7b7f35ef5d282ba58f1274598a39dad0ffedfba8","observation_id":"18d2132e-ae47-49d8-aad7-d30442014866","resolution":{"observed_at":"2026-08-10T17:52:49.028111Z","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":"2026-08-10T17:52:49.006332Z","title":"Network embedding as matrix factorization: Unify- ing DeepWalk, LINE, PTE, and node2vec,","venue":null,"work_id":"7c74720e-afcd-48d2-baef-69821cef2cc0","year":2018},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.468739Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:9808c04b0dfb3bb1dc6f42c1ec713fab356fecc3292753da484c026793e92128","observation_id":"5a75bc8b-41b9-4d82-864b-d86720ca806c","resolution":{"observed_at":"2026-08-10T17:52:49.011427Z","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":"1506.00019","last_updated":"2015-10-17T05:06:11Z","snapshot_observed_at":"2026-07-06T04:19:22.444137Z","submitted_at":"2015-05-29T20:16:51Z","title":"A Critical Review of Recurrent Neural Networks for Sequence Learning","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1506.00019","snapshot_observed_at":"2026-08-10T17:52:48.473346Z","title":"A critical review of recurrent neural networks for sequence learn- ing,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.473346Z"},"links":{"cited_paper":"/paper/1506.00019","citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:8ca473d32abb43e4e57517e9bd644bc7a1f556a9d059a08c482b81b107203843","observation_id":"a810be61-8aaa-48d4-9e47-0529e0a5a10e","resolution":{"observed_at":"2026-08-10T17:52:48.473346Z","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":"2026-08-10T17:52:48.991549Z","title":"Recurrent neural networks for multivariate time series with missing values,","venue":null,"work_id":"80391095-0f1c-4145-af4d-9ff286f7f086","year":2018},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.478261Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:5d629ef868e429c2e2a1bf457d68488deea90e504257faebf59af537a7b58255","observation_id":"51900177-80b6-4808-8938-582b0a90fde9","resolution":{"observed_at":"2026-08-10T17:52:48.996562Z","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":"2026-08-10T17:52:48.976055Z","title":"Neural hawkes process: A neurally self-modulating multivariate point process,","venue":null,"work_id":"db8d49f0-f006-4ba0-887b-b2db37d9419d","year":2017},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.482579Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:b34db67bc0e21987d7f1c538cfae1136f3d471b20cd6f92613f44a968ca47c02","observation_id":"d868cc0d-fcd3-49e0-bdb5-1cc71f30a7c3","resolution":{"observed_at":"2026-08-10T17:52:48.981704Z","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":"2026-08-10T17:52:48.960728Z","title":null,"venue":null,"work_id":"a278a432-a8ee-45bb-b8ff-55194fac7e4f","year":null},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.487332Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:48900fda872427b1615958a6757ddbbf37f041b0ed3d753e023c1cff56c7a361","observation_id":"e6e6be38-c673-46c7-a6a5-98f7252021a0","resolution":{"observed_at":"2026-08-10T17:52:48.965570Z","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":"2026-08-10T17:52:48.945221Z","title":"The process is repeated until the modularity Q no longer improves significantly","venue":null,"work_id":"4bb713e7-c98c-48d0-b5f1-993f85cfab77","year":null},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.492213Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:66a6085e73ee9320b398ce0147607d7284cea5b00f995eed9f3d3781e5501f56","observation_id":"f362ddbc-2413-4d4a-821c-e76bfba73566","resolution":{"observed_at":"2026-08-10T17:52:48.950336Z","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":"2026-08-10T17:52:48.929887Z","title":"For each node wi, the hidden state hi is computed as: hi = g(h′ i−1, A(wi)), (24) where: • h′ i−1 is the cumulative hidden state from the previous step","venue":null,"work_id":"73e37f7e-d831-49c0-aa1e-915c847d7384","year":null},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.498430Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:7353cfb04b5963ab8902a398cdce4474e80516ec3aa5c8f2fd33bfb27a018142","observation_id":"1f127111-8075-4729-aa94-4004d759adb7","resolution":{"observed_at":"2026-08-10T17:52:48.934724Z","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":"2026-08-10T17:52:48.914084Z","title":"Unlikeg, which incorporates node-specific input features,f focuses solely on temporal dynamics and acts on the output ofg","venue":null,"work_id":"bb1a70be-d6be-4fa2-a328-b9795fce3710","year":null},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.503292Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:8a6379139ef27eec3f9254da9c4a7f7c06989e7880e7d7563730df13e4617c3a","observation_id":"15703e60-5758-47e3-933e-f1c839f9c0b7","resolution":{"observed_at":"2026-08-10T17:52:48.919830Z","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":"2026-08-10T17:52:48.898468Z","title":null,"venue":null,"work_id":"4c9c4a9d-271f-403c-b4e7-3d02799f1f6a","year":null},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.510090Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:9ba0399ad797afe2c4844122336f67647033d5ea5a771ccfa7803fda7606551c","observation_id":"defcf31e-43c8-44c1-8697-2a4a637307c2","resolution":{"observed_at":"2026-08-10T17:52:48.903255Z","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":"2026-08-10T17:52:48.882262Z","title":null,"venue":null,"work_id":"a4462253-5da0-4c63-88f7-07e1545844b5","year":null},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.515131Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:fdad2d0fd7bcf86802461362174130bbe933ae8bba45372d8909d9bce7614c19","observation_id":"63df3959-be5f-4390-b284-42a469a3ea9c","resolution":{"observed_at":"2026-08-10T17:52:48.886896Z","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":"2026-08-10T17:52:48.867603Z","title":null,"venue":null,"work_id":"a1da15b6-baf4-45b5-b82d-c500c7baa3a6","year":null},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.520382Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:cb21bfd7efb01868d9982c2c6ac89fac0e05e3986d56d8b379d10cd7dceac454","observation_id":"ef5f82fd-73c5-46ec-b257-cd218b22298d","resolution":{"observed_at":"2026-08-10T17:52:48.872220Z","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":"2026-08-10T17:52:48.853465Z","title":null,"venue":null,"work_id":"2f4874be-42a6-4d01-bb38-691c13c3bd07","year":null},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.525987Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:b8ac93bdd3851b00a5c780112f65b3f57281cfc32f132c30ad3d44fdf8587660","observation_id":"1c92f3cb-b21e-41f2-8a62-a44923067801","resolution":{"observed_at":"2026-08-10T17:52:48.857861Z","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":"3190.8764","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T17:52:48.637205Z","title":null,"venue":null,"work_id":"cccad239-afc4-4d99-845e-4cf77d7fdf38","year":2000},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.531291Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:7e04a12ea2aa8318acbfa9fe1991ddad80f4a90303b392479aa3b0a596c498e3","observation_id":"ad0ad35f-18bd-4e39-9066-c45300a4a060","resolution":{"observed_at":"2026-08-10T17:52:48.646287Z","resolver_source":"raw_fallback","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":"2026-08-10T17:52:48.839158Z","title":"Anonymized Walk Construction","venue":null,"work_id":"40efd7f9-704b-4b53-b989-72b57ea1be66","year":null},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.537188Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:920d2bcfab6859c20fd2cad94522b86a9f1119add138f58b8eeccb81405845ae","observation_id":"f54955b0-3b2c-4965-bf0e-938eea79a25d","resolution":{"observed_at":"2026-08-10T17:52:48.843525Z","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":"2026-08-10T17:52:49.054119Z","title":null,"venue":null,"work_id":"634966ba-5174-4a56-9809-61f3030d9fda","year":null},"citing_paper":{"arxiv_id":"2501.11880","last_updated":"2025-01-21T04:16:46Z","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","version":1},"reference_index":2015,"source":"pdf_text","source_observed_at":"2026-08-10T17:52:48.453179Z"},"links":{"citing_paper":"/paper/2501.11880"},"observation_digest":"sha256:0f63c56975c6c9917e603eca87f6c0cd904388240c08078466269d97e3e69dcd","observation_id":"ae5342c1-4c8b-4cd4-a83e-bcad1785278c","resolution":{"observed_at":"2026-08-10T17:52:49.058275Z","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":"2501.11880","last_updated":"2025-01-21T04:16:46Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-12T16:07:00.699348Z","submitted_at":"2025-01-21T04:16:46Z","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs"},"reference_resolution":{"displayed":73,"state_counts":{"malformed_identifier":0,"metadata_mismatch":2,"parse_uncertain":0,"unresolved":11,"verified_exact":5,"verified_fuzzy":55},"total_outbound_references":73},"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 14 August 2026, this Paper Citation Record lists 73 of 73 outbound references and 0 inbound Pith citation observations for arXiv:2501.11880."}