{"as_of":"2026-08-15T07:48:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f1f91202786a66e1a12036c4025f414eaa2888037a12cb4210bff0db33ea7def","coverage":[{"denominator":32,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":32,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T19:27:40.394282Z","state":"measured"},{"denominator":32,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":32,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+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/2501.10010/citation-record","integrity":"/paper/2501.10010/integrity","json":"/paper/2501.10010/citation-record.json","paper":"/paper/2501.10010"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T19:27:41.500224Z","title":"Data augmentation for graph neural networks,","venue":null,"work_id":"e6b4c981-6f45-43c0-ae96-9205eb39a353","year":2020},"citing_paper":{"arxiv_id":"2501.10010","last_updated":"2025-01-17T07:48:18Z","snapshot_observed_at":"2026-08-10T23:03:20.373506Z","submitted_at":"2025-01-17T07:48:18Z","title":"Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:40.166967Z"},"links":{"citing_paper":"/paper/2501.10010"},"observation_digest":"sha256:1eca24acadbf928c8aa32cffbbe836682a74b824198316b7a458df98d3f2b121","observation_id":"f5fc285b-48a5-4873-b32d-b40835648fb6","resolution":{"observed_at":"2026-08-10T19:27:41.508526Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T19:27:41.474852Z","title":"Graph contrastive learning with adaptive augmentation,","venue":null,"work_id":"0979b46e-de66-4cc1-b63e-8bfcc086611a","year":2021},"citing_paper":{"arxiv_id":"2501.10010","last_updated":"2025-01-17T07:48:18Z","snapshot_observed_at":"2026-08-10T23:03:20.373506Z","submitted_at":"2025-01-17T07:48:18Z","title":"Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:40.173287Z"},"links":{"citing_paper":"/paper/2501.10010"},"observation_digest":"sha256:4ba5a92b79791df943248297b9f97d3f4d2bb5b3be9948c2ebdfb65f4b25528a","observation_id":"cb84f14c-c8a7-446d-ac83-1916ef14ef0f","resolution":{"observed_at":"2026-08-10T19:27:41.481206Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2212.01026","last_updated":"2022-12-02T08:48:11Z","snapshot_observed_at":"2026-08-14T06:41:15.688973Z","submitted_at":"2022-12-02T08:48:11Z","title":"Spectral Feature Augmentation for Graph Contrastive Learning and Beyond","version":1},"cited_work":{"arxiv_id":"2212.01026","doi":null,"metadata_source":"pith","pith_arxiv_id":"2212.01026","snapshot_observed_at":"2026-08-10T19:27:40.700061Z","title":"Spectral Feature Augmentation for Graph Contrastive Learning and Beyond","venue":"cs.LG","work_id":"61e2b4e7-27c7-4ad6-9b4a-2c5b2dba7d7d","year":2022},"citing_paper":{"arxiv_id":"2501.10010","last_updated":"2025-01-17T07:48:18Z","snapshot_observed_at":"2026-08-10T23:03:20.373506Z","submitted_at":"2025-01-17T07:48:18Z","title":"Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:40.181982Z"},"links":{"cited_paper":"/paper/2212.01026","citing_paper":"/paper/2501.10010"},"observation_digest":"sha256:4b298a4ed0b3a205961a6aa1ba3686b590875304a08553813a5555a9feebcd00","observation_id":"f94b7eab-7155-4cbf-a8ab-194da52b1306","resolution":{"observed_at":"2026-08-10T19:27:40.706986Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T19:27:41.447590Z","title":"Unleashing the power of graph data augmentation on covariate distribution shift,","venue":null,"work_id":"00c5c5e3-f00e-47d7-af41-3ac366c8721b","year":2022},"citing_paper":{"arxiv_id":"2501.10010","last_updated":"2025-01-17T07:48:18Z","snapshot_observed_at":"2026-08-10T23:03:20.373506Z","submitted_at":"2025-01-17T07:48:18Z","title":"Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:40.190197Z"},"links":{"citing_paper":"/paper/2501.10010"},"observation_digest":"sha256:e85c32d7ac9cec68d495f4c9e85015c086d9b33fcdc68d3a708d2272c3c00d13","observation_id":"d2804613-49c8-4ff3-afcf-5cb14039ecd1","resolution":{"observed_at":"2026-08-10T19:27:41.458352Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T19:27:41.412890Z","title":"Structured sequence modeling with graph convolutional recurrent networks,","venue":null,"work_id":"634de417-da49-4c8a-8a47-814f02fc54aa","year":2016},"citing_paper":{"arxiv_id":"2501.10010","last_updated":"2025-01-17T07:48:18Z","snapshot_observed_at":"2026-08-10T23:03:20.373506Z","submitted_at":"2025-01-17T07:48:18Z","title":"Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:40.202299Z"},"links":{"citing_paper":"/paper/2501.10010"},"observation_digest":"sha256:c399551ca5f83d7758938d7d0ecda456ea0a3a26a34fce8294831272ed8a6c05","observation_id":"5cba2b86-e007-481f-9988-ccd3e4c9c7e3","resolution":{"observed_at":"2026-08-10T19:27:41.424374Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1902.10191","last_updated":"2019-11-18T18:42:50Z","snapshot_observed_at":"2026-08-15T01:32:13.981352Z","submitted_at":"2019-02-26T20:07:34Z","title":"EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.10191","snapshot_observed_at":"2026-08-10T19:27:40.212362Z","title":"Evolvegcn: Evolving graph convolutional networks for dynamic graphs,","venue":null,"work_id":null,"year":1902},"citing_paper":{"arxiv_id":"2501.10010","last_updated":"2025-01-17T07:48:18Z","snapshot_observed_at":"2026-08-10T23:03:20.373506Z","submitted_at":"2025-01-17T07:48:18Z","title":"Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:40.212362Z"},"links":{"cited_paper":"/paper/1902.10191","citing_paper":"/paper/2501.10010"},"observation_digest":"sha256:499f68aaf4af807a8a10099569eebbf9dbf8cba4c8f16214601107036777a957","observation_id":"630f3c57-c6f8-4600-a8a5-dec5a591ea50","resolution":{"observed_at":"2026-08-10T19:27:40.212362Z","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":"2026-08-10T19:27:41.384456Z","title":"Dysat: Deep neural representation learning on dynamic graphs via self- attention networks,","venue":null,"work_id":"4950395a-717a-4c89-80b9-e624ac567f4f","year":2020},"citing_paper":{"arxiv_id":"2501.10010","last_updated":"2025-01-17T07:48:18Z","snapshot_observed_at":"2026-08-10T23:03:20.373506Z","submitted_at":"2025-01-17T07:48:18Z","title":"Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:40.220264Z"},"links":{"citing_paper":"/paper/2501.10010"},"observation_digest":"sha256:8f8ca889781b499cbb13d60acec1fa27bc872aea7d462c7fba4bafc324822b94","observation_id":"21cfa016-92dd-42a6-9ddc-55784b769f72","resolution":{"observed_at":"2026-08-10T19:27:41.396399Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T19:27:41.348132Z","title":"Wingnn: Dynamic graph neural networks with random gradient aggregation window,","venue":null,"work_id":"1da9104e-2a9f-439f-9c77-a0b46d824cbf","year":2023},"citing_paper":{"arxiv_id":"2501.10010","last_updated":"2025-01-17T07:48:18Z","snapshot_observed_at":"2026-08-10T23:03:20.373506Z","submitted_at":"2025-01-17T07:48:18Z","title":"Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:40.225699Z"},"links":{"citing_paper":"/paper/2501.10010"},"observation_digest":"sha256:746f01d79bb54351bab7ff5eb0876698b6c9604097f1661158adf74e61ea9da6","observation_id":"0ce69d96-9737-4c9e-9ed1-8eebbcc8337f","resolution":{"observed_at":"2026-08-10T19:27:41.360605Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T19:27:41.323542Z","title":"Todynet: temporal dynamic graph neural network for multivariate time series classification,","venue":null,"work_id":"f009fc2b-f87e-4de3-8b12-8688c6d4e2f4","year":2024},"citing_paper":{"arxiv_id":"2501.10010","last_updated":"2025-01-17T07:48:18Z","snapshot_observed_at":"2026-08-10T23:03:20.373506Z","submitted_at":"2025-01-17T07:48:18Z","title":"Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:40.233179Z"},"links":{"citing_paper":"/paper/2501.10010"},"observation_digest":"sha256:8d544259f85c9b19c0f8288f21270123e6f0b438ade1be48accc4bb37bd58413","observation_id":"f8c388d2-653a-411c-8709-d0e26399df0b","resolution":{"observed_at":"2026-08-10T19:27:41.333466Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T19:27:41.280847Z","title":"Adaptive data augmentation on temporal graphs,","venue":null,"work_id":"86667c4c-e149-4753-8379-200fbe0fe73e","year":2021},"citing_paper":{"arxiv_id":"2501.10010","last_updated":"2025-01-17T07:48:18Z","snapshot_observed_at":"2026-08-10T23:03:20.373506Z","submitted_at":"2025-01-17T07:48:18Z","title":"Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:40.238268Z"},"links":{"citing_paper":"/paper/2501.10010"},"observation_digest":"sha256:42250348c9fd75316a1ff014a82ebe09e82c7b6a10f30fb695156513e723829c","observation_id":"1439a607-399b-451f-ac20-ffdf5333e033","resolution":{"observed_at":"2026-08-10T19:27:41.304573Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T19:27:41.252585Z","title":"Time-aware random walk diffusion to improve dynamic graph learning,","venue":null,"work_id":"6448f248-d4d0-4b8e-b448-3feb892efcdb","year":2023},"citing_paper":{"arxiv_id":"2501.10010","last_updated":"2025-01-17T07:48:18Z","snapshot_observed_at":"2026-08-10T23:03:20.373506Z","submitted_at":"2025-01-17T07:48:18Z","title":"Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:40.243406Z"},"links":{"citing_paper":"/paper/2501.10010"},"observation_digest":"sha256:692dfa3e09be6cef7c590a1fc97a3e171c1dc0526a66828f956120c7bb9d351f","observation_id":"e6a8089a-4375-47a7-bb94-3424c658e14f","resolution":{"observed_at":"2026-08-10T19:27:41.259326Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T19:27:41.226855Z","title":"Temporal graph representation learning with adaptive augmentation contrastive,","venue":null,"work_id":"4e692bc1-3911-40a3-a08c-556f4aca7eb7","year":null},"citing_paper":{"arxiv_id":"2501.10010","last_updated":"2025-01-17T07:48:18Z","snapshot_observed_at":"2026-08-10T23:03:20.373506Z","submitted_at":"2025-01-17T07:48:18Z","title":"Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:40.248785Z"},"links":{"citing_paper":"/paper/2501.10010"},"observation_digest":"sha256:235b7e78deedcd3cd5fa1756c7d66283713355da7ff1f911b541c8eafc31acee","observation_id":"fc4dbdfc-c47f-4ba4-bf1f-c29f734e0fc6","resolution":{"observed_at":"2026-08-10T19:27:41.235735Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T19:27:41.153424Z","title":"Latent diffusion- based data augmentation for continuous-time dynamic graph model,","venue":null,"work_id":"e30e0b23-6640-41a1-bdac-cda21c061c42","year":2024},"citing_paper":{"arxiv_id":"2501.10010","last_updated":"2025-01-17T07:48:18Z","snapshot_observed_at":"2026-08-10T23:03:20.373506Z","submitted_at":"2025-01-17T07:48:18Z","title":"Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:40.261017Z"},"links":{"citing_paper":"/paper/2501.10010"},"observation_digest":"sha256:90b4752d796e24f1b3ccade9cf660c56538e1a7b69647e9d8a97bcf8b3aff825","observation_id":"5ef390a5-bec4-49ad-ba5a-9d51453fe717","resolution":{"observed_at":"2026-08-10T19:27:41.168199Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T19:27:41.124999Z","title":"Rdgsl: Dynamic graph representation learning with structure learning,","venue":null,"work_id":"280a1afc-ae62-4480-9d7d-3d63966c339b","year":2023},"citing_paper":{"arxiv_id":"2501.10010","last_updated":"2025-01-17T07:48:18Z","snapshot_observed_at":"2026-08-10T23:03:20.373506Z","submitted_at":"2025-01-17T07:48:18Z","title":"Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:40.266819Z"},"links":{"citing_paper":"/paper/2501.10010"},"observation_digest":"sha256:08fcd668d83a74400ce68cd8320f6cfe255ec8cc120c9f4f0351e86c0d73e66e","observation_id":"99a1ad52-e08b-4468-a248-629f153cc61b","resolution":{"observed_at":"2026-08-10T19:27:41.133262Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T19:27:41.073036Z","title":"Representation learning for dynamic graphs: A survey,","venue":null,"work_id":"5c94321c-45ee-43df-be93-6878d530a6db","year":null},"citing_paper":{"arxiv_id":"2501.10010","last_updated":"2025-01-17T07:48:18Z","snapshot_observed_at":"2026-08-10T23:03:20.373506Z","submitted_at":"2025-01-17T07:48:18Z","title":"Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:40.274080Z"},"links":{"citing_paper":"/paper/2501.10010"},"observation_digest":"sha256:bf2d160c0af84cf05d096ac0c733b18afbde3db1b7e485ef40ebce650fc4faa5","observation_id":"4aa46d5a-24fa-4aa8-9980-537355f39940","resolution":{"observed_at":"2026-08-10T19:27:41.086785Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"0912.3848","last_updated":"2009-12-19T00:50:02Z","snapshot_observed_at":"2026-08-15T05:30:50.345255Z","submitted_at":"2009-12-19T00:50:02Z","title":"Wavelets on Graphs via Spectral Graph Theory","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"0912.3848","snapshot_observed_at":"2026-08-10T19:27:40.289011Z","title":"Wavelets on graphs via spectral graph theory,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2501.10010","last_updated":"2025-01-17T07:48:18Z","snapshot_observed_at":"2026-08-10T23:03:20.373506Z","submitted_at":"2025-01-17T07:48:18Z","title":"Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:40.289011Z"},"links":{"cited_paper":"/paper/0912.3848","citing_paper":"/paper/2501.10010"},"observation_digest":"sha256:b0539413ae101d40f9db5a496c79fd8b8a1e4a46b013ea8fe77741725dee5bcb","observation_id":"daea6694-7edc-4691-aaeb-57481556afbc","resolution":{"observed_at":"2026-08-10T19:27:40.289011Z","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":"2026-08-10T19:27:40.961790Z","title":"Wavelet-based visual analysis of dynamic networks,","venue":null,"work_id":"ad441c12-9526-4403-8b6e-3cea0307c92d","year":2018},"citing_paper":{"arxiv_id":"2501.10010","last_updated":"2025-01-17T07:48:18Z","snapshot_observed_at":"2026-08-10T23:03:20.373506Z","submitted_at":"2025-01-17T07:48:18Z","title":"Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:40.298053Z"},"links":{"citing_paper":"/paper/2501.10010"},"observation_digest":"sha256:f6deaa7ca435086d20711ec73b7f80c2903b6cf545e8265d582b398d44d43ded","observation_id":"530b3f1f-2a10-4475-9422-0d58b8bf2e1e","resolution":{"observed_at":"2026-08-10T19:27:40.968522Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T19:27:40.936757Z","title":"Discrete signal processing on graphs: Frequency analysis,","venue":null,"work_id":"cb0d0490-555e-416e-90d1-f8cab913039f","year":2013},"citing_paper":{"arxiv_id":"2501.10010","last_updated":"2025-01-17T07:48:18Z","snapshot_observed_at":"2026-08-10T23:03:20.373506Z","submitted_at":"2025-01-17T07:48:18Z","title":"Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:40.304094Z"},"links":{"citing_paper":"/paper/2501.10010"},"observation_digest":"sha256:f4a08d4453335298c035c8103e724a8c547ee3e6e857d5c4bb8356e8d7b408aa","observation_id":"04d2e266-35d5-4fa3-9017-0587ee096192","resolution":{"observed_at":"2026-08-10T19:27:40.943046Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1509.00016","last_updated":"2017-05-22T15:24:00Z","snapshot_observed_at":"2026-08-14T22:34:25.656282Z","submitted_at":"2015-08-31T20:10:24Z","title":"Localization in Seeded PageRank","version":4},"cited_work":{"arxiv_id":"1509.00016","doi":null,"metadata_source":"pith","pith_arxiv_id":"1509.00016","snapshot_observed_at":"2026-08-10T19:27:40.613041Z","title":"Localization in Seeded PageRank","venue":"cs.SI","work_id":"79cd862b-70c9-4b9f-be6a-9c717767a3d4","year":2015},"citing_paper":{"arxiv_id":"2501.10010","last_updated":"2025-01-17T07:48:18Z","snapshot_observed_at":"2026-08-10T23:03:20.373506Z","submitted_at":"2025-01-17T07:48:18Z","title":"Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:40.311395Z"},"links":{"cited_paper":"/paper/1509.00016","citing_paper":"/paper/2501.10010"},"observation_digest":"sha256:388d34f3b2f527040851801292387c88d461442c751fa8537b74345f58f7f55a","observation_id":"c6a0df4c-bdc1-4abf-b259-a1ff31244a00","resolution":{"observed_at":"2026-08-10T19:27:40.621234Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2211.01214","last_updated":"2023-02-14T03:17:59Z","snapshot_observed_at":"2026-08-13T13:50:45.623619Z","submitted_at":"2022-11-02T15:55:46Z","title":"Time-aware Random Walk Diffusion to Improve Dynamic Graph Learning","version":5},"cited_work":{"arxiv_id":"2211.01214","doi":null,"metadata_source":"pith","pith_arxiv_id":"2211.01214","snapshot_observed_at":"2026-08-10T19:27:40.564659Z","title":"Time-aware Random Walk Diffusion to Improve Dynamic Graph Learning","venue":"cs.LG","work_id":"cf6354e4-15b4-470b-9996-d48211c5f48b","year":2022},"citing_paper":{"arxiv_id":"2501.10010","last_updated":"2025-01-17T07:48:18Z","snapshot_observed_at":"2026-08-10T23:03:20.373506Z","submitted_at":"2025-01-17T07:48:18Z","title":"Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:40.322484Z"},"links":{"cited_paper":"/paper/2211.01214","citing_paper":"/paper/2501.10010"},"observation_digest":"sha256:aa1d22825cb9c1183504d1eca94a926e68180dc16247ceef597bb9e94afc82e6","observation_id":"eceb69fd-57ad-4052-a431-97965a278861","resolution":{"observed_at":"2026-08-10T19:27:40.577320Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T19:27:40.908324Z","title":"Edge weight prediction in weighted signed networks,","venue":null,"work_id":"10ebcdbd-9c50-4185-870f-12b6c3cb0db9","year":2016},"citing_paper":{"arxiv_id":"2501.10010","last_updated":"2025-01-17T07:48:18Z","snapshot_observed_at":"2026-08-10T23:03:20.373506Z","submitted_at":"2025-01-17T07:48:18Z","title":"Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:40.330202Z"},"links":{"citing_paper":"/paper/2501.10010"},"observation_digest":"sha256:16b7f6c01c9ce47aceba33c172a267278933c298395d5e2f3d0c1f1046e793f4","observation_id":"a73b79c4-de14-4818-aee7-2f108d4c5178","resolution":{"observed_at":"2026-08-10T19:27:40.917294Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1003.2429","last_updated":"2010-03-11T21:27:11Z","snapshot_observed_at":"2026-08-15T06:16:39.534569Z","submitted_at":"2010-03-11T21:27:11Z","title":"Predicting Positive and Negative Links in Online Social Networks","version":1},"cited_work":{"arxiv_id":"1003.2429","doi":null,"metadata_source":"pith","pith_arxiv_id":"1003.2429","snapshot_observed_at":"2026-08-10T19:27:40.508323Z","title":"Predicting Positive and Negative Links in Online Social Networks","venue":"physics.soc-ph","work_id":"5d677901-06c6-46c3-9370-64992e4a294b","year":2010},"citing_paper":{"arxiv_id":"2501.10010","last_updated":"2025-01-17T07:48:18Z","snapshot_observed_at":"2026-08-10T23:03:20.373506Z","submitted_at":"2025-01-17T07:48:18Z","title":"Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:40.336232Z"},"links":{"cited_paper":"/paper/1003.2429","citing_paper":"/paper/2501.10010"},"observation_digest":"sha256:fdb5da19fca73c9fa0010d35cb2f0881892a565fe3e30653c967280c4fa0ac1e","observation_id":"f0c63702-c79d-49a7-91ca-137c8316caf1","resolution":{"observed_at":"2026-08-10T19:27:40.520198Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T19:27:40.882633Z","title":"Community interaction and conflict on the web,","venue":null,"work_id":"9a96f3fe-53a6-4dc9-acb3-36a427e0ed4c","year":2018},"citing_paper":{"arxiv_id":"2501.10010","last_updated":"2025-01-17T07:48:18Z","snapshot_observed_at":"2026-08-10T23:03:20.373506Z","submitted_at":"2025-01-17T07:48:18Z","title":"Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:40.341678Z"},"links":{"citing_paper":"/paper/2501.10010"},"observation_digest":"sha256:409217d6c7d97a05346e8800ac5157a5449b4556261df8973437f18d6616ce71","observation_id":"894ceb4c-3fc4-425b-90c7-ef3dde66e4e3","resolution":{"observed_at":"2026-08-10T19:27:40.890986Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T19:27:40.856619Z","title":"Spatio-temporal attentive rnn for node classification in temporal attributed graphs,","venue":null,"work_id":"ef9caafc-a883-4e9b-8047-c44a89f475f0","year":2019},"citing_paper":{"arxiv_id":"2501.10010","last_updated":"2025-01-17T07:48:18Z","snapshot_observed_at":"2026-08-10T23:03:20.373506Z","submitted_at":"2025-01-17T07:48:18Z","title":"Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:40.347689Z"},"links":{"citing_paper":"/paper/2501.10010"},"observation_digest":"sha256:8fcf83f86c13e2fa3b6989a8aba33fb0ab6afd9d156283993feb113b5624777f","observation_id":"9ac0c04c-ec1a-44c6-aef6-ce15d43e939d","resolution":{"observed_at":"2026-08-10T19:27:40.863954Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1609.02907","last_updated":"2017-02-22T09:55:36Z","snapshot_observed_at":"2026-08-13T11:38:10.906031Z","submitted_at":"2016-09-09T19:48:41Z","title":"Semi-Supervised Classification with Graph Convolutional Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1609.02907","snapshot_observed_at":"2026-08-10T19:27:40.352964Z","title":"Semi-supervised classification with graph convolutional networks,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2501.10010","last_updated":"2025-01-17T07:48:18Z","snapshot_observed_at":"2026-08-10T23:03:20.373506Z","submitted_at":"2025-01-17T07:48:18Z","title":"Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:40.352964Z"},"links":{"cited_paper":"/paper/1609.02907","citing_paper":"/paper/2501.10010"},"observation_digest":"sha256:ef97d4f2a5f03f528e1c4d91b7d1dcadccb412a5d428641b688ae6e2289d4f47","observation_id":"c9048e16-9df5-4109-8d9a-b85aec38fc28","resolution":{"observed_at":"2026-08-10T19:27:40.352964Z","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":"2026-08-10T19:27:40.832320Z","title":"Roland: graph learning framework for dynamic graphs,","venue":null,"work_id":"9b077e40-703d-48fd-b91a-bdb432f6ffa8","year":2022},"citing_paper":{"arxiv_id":"2501.10010","last_updated":"2025-01-17T07:48:18Z","snapshot_observed_at":"2026-08-10T23:03:20.373506Z","submitted_at":"2025-01-17T07:48:18Z","title":"Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:40.358686Z"},"links":{"citing_paper":"/paper/2501.10010"},"observation_digest":"sha256:260485983bce6bf92ce8f8f1ecec8cc0b31de8857face7ec67f88733a6028018","observation_id":"fab0b252-dc27-4a87-a301-e490b167f711","resolution":{"observed_at":"2026-08-10T19:27:40.840913Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T19:27:40.798840Z","title":"High-order topology- enhanced graph convolutional networks for dynamic graphs,","venue":null,"work_id":"9b7aac5c-cee9-4f85-9f09-3c81451999ce","year":2022},"citing_paper":{"arxiv_id":"2501.10010","last_updated":"2025-01-17T07:48:18Z","snapshot_observed_at":"2026-08-10T23:03:20.373506Z","submitted_at":"2025-01-17T07:48:18Z","title":"Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:40.366287Z"},"links":{"citing_paper":"/paper/2501.10010"},"observation_digest":"sha256:80ca12258b7220f311e08977e84443c61c15a30e1a4f874d01d432dd2dc8c84c","observation_id":"cbb334ea-176e-4341-8653-bd0a60e9b2a1","resolution":{"observed_at":"2026-08-10T19:27:40.808342Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T19:27:40.773429Z","title":"Dergcn: Dynamic-evolving graph convolutional networks for human trajectory prediction,","venue":null,"work_id":"a2115f01-ebd8-4ca2-9e03-975c30db68e7","year":2024},"citing_paper":{"arxiv_id":"2501.10010","last_updated":"2025-01-17T07:48:18Z","snapshot_observed_at":"2026-08-10T23:03:20.373506Z","submitted_at":"2025-01-17T07:48:18Z","title":"Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:40.374093Z"},"links":{"citing_paper":"/paper/2501.10010"},"observation_digest":"sha256:53a79cdc3564d25d0bef58a1bca15138eefedced7c8b0525d97c40773fb8596f","observation_id":"15805586-431f-4d57-96fc-2a03d5549c58","resolution":{"observed_at":"2026-08-10T19:27:40.779511Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T19:27:40.744536Z","title":"Pytorch geometric temporal: Spatiotemporal signal processing with neural machine learning models,","venue":null,"work_id":"80e450f4-8b7e-4662-8439-3e37549e8f98","year":2021},"citing_paper":{"arxiv_id":"2501.10010","last_updated":"2025-01-17T07:48:18Z","snapshot_observed_at":"2026-08-10T23:03:20.373506Z","submitted_at":"2025-01-17T07:48:18Z","title":"Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:40.383453Z"},"links":{"citing_paper":"/paper/2501.10010"},"observation_digest":"sha256:9f417a388a25c75e7b22c9b20168f6ab06526e2d1e9a67ccb35e2c2927421ec1","observation_id":"b41627c2-88d3-4ad4-b637-e502475a63bc","resolution":{"observed_at":"2026-08-10T19:27:40.752406Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T19:27:40.723395Z","title":"Deep graph library: A graph-centric, highly-performant package for graph neural networks,","venue":null,"work_id":"38b4247c-a6dc-4744-8e17-ead25c50480c","year":2019},"citing_paper":{"arxiv_id":"2501.10010","last_updated":"2025-01-17T07:48:18Z","snapshot_observed_at":"2026-08-10T23:03:20.373506Z","submitted_at":"2025-01-17T07:48:18Z","title":"Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:40.394282Z"},"links":{"citing_paper":"/paper/2501.10010"},"observation_digest":"sha256:bf160e4ba9db3063ccfcb6045a6194b5cab48085d14310b284fc43432e4ec530","observation_id":"865b3de4-140b-4284-aadd-80047e6381b6","resolution":{"observed_at":"2026-08-10T19:27:40.729187Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T19:27:41.036986Z","title":"Available: https://api.semanticscholar.org/CorpusID: 216608194","venue":null,"work_id":"69dfcb23-7932-40e9-955f-e059c8f82023","year":null},"citing_paper":{"arxiv_id":"2501.10010","last_updated":"2025-01-17T07:48:18Z","snapshot_observed_at":"2026-08-10T23:03:20.373506Z","submitted_at":"2025-01-17T07:48:18Z","title":"Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:40.279918Z"},"links":{"citing_paper":"/paper/2501.10010"},"observation_digest":"sha256:ff0904b82ae8f168bab9d7e5ab320ece14153ac7812f33ce3881b35951f39dd7","observation_id":"8f933dc9-1d3b-49db-9f46-2fd8b173eccc","resolution":{"observed_at":"2026-08-10T19:27:41.043033Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T19:27:41.192778Z","title":"Available: https://api.semanticscholar.org/CorpusID: 262088334","venue":null,"work_id":"e0495a08-12aa-46b5-83df-00dc34c2a9e3","year":null},"citing_paper":{"arxiv_id":"2501.10010","last_updated":"2025-01-17T07:48:18Z","snapshot_observed_at":"2026-08-10T23:03:20.373506Z","submitted_at":"2025-01-17T07:48:18Z","title":"Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-10T19:27:40.254984Z"},"links":{"citing_paper":"/paper/2501.10010"},"observation_digest":"sha256:831f989a749236bc0758d7fb5b46c689fa51333d7c3916ee43431c8f6e41abfe","observation_id":"ee149409-0b61-462c-8cc9-635f19db5dc9","resolution":{"observed_at":"2026-08-10T19:27:41.204276Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.10010","last_updated":"2025-01-17T07:48:18Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-10T23:03:20.373506Z","submitted_at":"2025-01-17T07:48:18Z","title":"Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning"},"reference_resolution":{"displayed":32,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":3,"verified_exact":4,"verified_fuzzy":25},"total_outbound_references":32},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2501.10010."}