{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:XXZTKBFAOPEM6VUJUATH5DYD42","short_pith_number":"pith:XXZTKBFA","canonical_record":{"source":{"id":"2501.10010","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-01-17T07:48:18Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"876eb9ef260cf4b30a23b5f9036e563a5f07fcee3f533dfb9328abfed2d8e0d3","abstract_canon_sha256":"3483a2224b5d7baeb8beeb065b073f208a57723da5f5ce83abbd252dcd4c9b97"},"schema_version":"1.0"},"canonical_sha256":"bdf33504a073c8cf5689a0267e8f03e680e56728b5c4ec9ddb9f22a351de555f","source":{"kind":"arxiv","id":"2501.10010","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.10010","created_at":"2026-07-05T10:02:15Z"},{"alias_kind":"arxiv_version","alias_value":"2501.10010v1","created_at":"2026-07-05T10:02:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.10010","created_at":"2026-07-05T10:02:15Z"},{"alias_kind":"pith_short_12","alias_value":"XXZTKBFAOPEM","created_at":"2026-07-05T10:02:15Z"},{"alias_kind":"pith_short_16","alias_value":"XXZTKBFAOPEM6VUJ","created_at":"2026-07-05T10:02:15Z"},{"alias_kind":"pith_short_8","alias_value":"XXZTKBFA","created_at":"2026-07-05T10:02:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:XXZTKBFAOPEM6VUJUATH5DYD42","target":"record","payload":{"canonical_record":{"source":{"id":"2501.10010","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-01-17T07:48:18Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"876eb9ef260cf4b30a23b5f9036e563a5f07fcee3f533dfb9328abfed2d8e0d3","abstract_canon_sha256":"3483a2224b5d7baeb8beeb065b073f208a57723da5f5ce83abbd252dcd4c9b97"},"schema_version":"1.0"},"canonical_sha256":"bdf33504a073c8cf5689a0267e8f03e680e56728b5c4ec9ddb9f22a351de555f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:02:15.058690Z","signature_b64":"J3HX3xTlh3Wnrs9eEZQSJ0IjLOJRPCpzp1mXe/RqvszmaY99RIUmnnrYZICwGeo0R7YVWc36R7XcVEYW4VR7Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bdf33504a073c8cf5689a0267e8f03e680e56728b5c4ec9ddb9f22a351de555f","last_reissued_at":"2026-07-05T10:02:15.058199Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:02:15.058199Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2501.10010","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:02:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LLZ83tGSrz5qfukHGTdB9mVTgV1sJ3Yp+/euOTVrwDbJrgeTgWa05QjlhxvE7c5Z4CLIU3cyHSovKUt20r6wCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T11:02:22.048227Z"},"content_sha256":"e2dc66eb3d01a87a57d41e56e2ae6527bd9ce9dc904c294fdcd664154f18f86d","schema_version":"1.0","event_id":"sha256:e2dc66eb3d01a87a57d41e56e2ae6527bd9ce9dc904c294fdcd664154f18f86d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:XXZTKBFAOPEM6VUJUATH5DYD42","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Bingce Wang, Hanlin Xue, Tong Mo, Tuoyu Feng, Weiping Li, Xiaoyang Liu, Xu Chu, Zhijie Tan","submitted_at":"2025-01-17T07:48:18Z","abstract_excerpt":"Dynamic graph augmentation is used to improve the performance of dynamic GNNs. Most methods assume temporal locality, meaning that recent edges are more influential than earlier edges. However, for temporal changes in edges caused by random noise, overemphasizing recent edges while neglecting earlier ones may lead to the model capturing noise. To address this issue, we propose STAA (SpatioTemporal Activity-Aware Random Walk Diffusion). STAA identifies nodes likely to have noisy edges in spatiotemporal dimensions. Spatially, it analyzes critical topological positions through graph wavelet coeff"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.10010","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2501.10010/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:02:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vOE6FATai2Auv/53xhItuLfCylPfK1GS/7rtvsiYMzif+z7Sv4Qvyl9Ww4cZ/SdKEqqRgL5Pxbaqa/RrIbikBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T11:02:22.048744Z"},"content_sha256":"7d42ba85180eef8a5900322c1cd7f129fe646ad2cc686a1de51ab78c48006c9d","schema_version":"1.0","event_id":"sha256:7d42ba85180eef8a5900322c1cd7f129fe646ad2cc686a1de51ab78c48006c9d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XXZTKBFAOPEM6VUJUATH5DYD42/bundle.json","state_url":"https://pith.science/pith/XXZTKBFAOPEM6VUJUATH5DYD42/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XXZTKBFAOPEM6VUJUATH5DYD42/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-09T11:02:22Z","links":{"resolver":"https://pith.science/pith/XXZTKBFAOPEM6VUJUATH5DYD42","bundle":"https://pith.science/pith/XXZTKBFAOPEM6VUJUATH5DYD42/bundle.json","state":"https://pith.science/pith/XXZTKBFAOPEM6VUJUATH5DYD42/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XXZTKBFAOPEM6VUJUATH5DYD42/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:XXZTKBFAOPEM6VUJUATH5DYD42","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"3483a2224b5d7baeb8beeb065b073f208a57723da5f5ce83abbd252dcd4c9b97","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-01-17T07:48:18Z","title_canon_sha256":"876eb9ef260cf4b30a23b5f9036e563a5f07fcee3f533dfb9328abfed2d8e0d3"},"schema_version":"1.0","source":{"id":"2501.10010","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.10010","created_at":"2026-07-05T10:02:15Z"},{"alias_kind":"arxiv_version","alias_value":"2501.10010v1","created_at":"2026-07-05T10:02:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.10010","created_at":"2026-07-05T10:02:15Z"},{"alias_kind":"pith_short_12","alias_value":"XXZTKBFAOPEM","created_at":"2026-07-05T10:02:15Z"},{"alias_kind":"pith_short_16","alias_value":"XXZTKBFAOPEM6VUJ","created_at":"2026-07-05T10:02:15Z"},{"alias_kind":"pith_short_8","alias_value":"XXZTKBFA","created_at":"2026-07-05T10:02:15Z"}],"graph_snapshots":[{"event_id":"sha256:7d42ba85180eef8a5900322c1cd7f129fe646ad2cc686a1de51ab78c48006c9d","target":"graph","created_at":"2026-07-05T10:02:15Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2501.10010/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Dynamic graph augmentation is used to improve the performance of dynamic GNNs. Most methods assume temporal locality, meaning that recent edges are more influential than earlier edges. However, for temporal changes in edges caused by random noise, overemphasizing recent edges while neglecting earlier ones may lead to the model capturing noise. To address this issue, we propose STAA (SpatioTemporal Activity-Aware Random Walk Diffusion). STAA identifies nodes likely to have noisy edges in spatiotemporal dimensions. Spatially, it analyzes critical topological positions through graph wavelet coeff","authors_text":"Bingce Wang, Hanlin Xue, Tong Mo, Tuoyu Feng, Weiping Li, Xiaoyang Liu, Xu Chu, Zhijie Tan","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-01-17T07:48:18Z","title":"Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.10010","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:e2dc66eb3d01a87a57d41e56e2ae6527bd9ce9dc904c294fdcd664154f18f86d","target":"record","created_at":"2026-07-05T10:02:15Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"3483a2224b5d7baeb8beeb065b073f208a57723da5f5ce83abbd252dcd4c9b97","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-01-17T07:48:18Z","title_canon_sha256":"876eb9ef260cf4b30a23b5f9036e563a5f07fcee3f533dfb9328abfed2d8e0d3"},"schema_version":"1.0","source":{"id":"2501.10010","kind":"arxiv","version":1}},"canonical_sha256":"bdf33504a073c8cf5689a0267e8f03e680e56728b5c4ec9ddb9f22a351de555f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bdf33504a073c8cf5689a0267e8f03e680e56728b5c4ec9ddb9f22a351de555f","first_computed_at":"2026-07-05T10:02:15.058199Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:02:15.058199Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"J3HX3xTlh3Wnrs9eEZQSJ0IjLOJRPCpzp1mXe/RqvszmaY99RIUmnnrYZICwGeo0R7YVWc36R7XcVEYW4VR7Cg==","signature_status":"signed_v1","signed_at":"2026-07-05T10:02:15.058690Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.10010","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e2dc66eb3d01a87a57d41e56e2ae6527bd9ce9dc904c294fdcd664154f18f86d","sha256:7d42ba85180eef8a5900322c1cd7f129fe646ad2cc686a1de51ab78c48006c9d"],"state_sha256":"31fccfea52baa7554aa6b0f4f0bc92981c9784ba0ee59ac4a2aa50c1795e2435"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UmLTjY0B5AP5r8e+e4MagnHQtqiDUZMbozm9q5ML5CFUi4lsrKnYqdvcGOiJO7rGE3P5vueMXdxS9EPMWlR2Cg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T11:02:22.054732Z","bundle_sha256":"6e8d43a5302a0ae8c5e4cc5c11f05479817be92201ad6479f95a4d361a9a77b1"}}