{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:5OW2BIO2LYBENUF6LSWDDATP7H","short_pith_number":"pith:5OW2BIO2","canonical_record":{"source":{"id":"2402.05396","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-08T04:16:35Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"f5ac2eaec609b7784df018f7eb3c99c0df29b7c7d7f3cd1b6a32408229d7afe7","abstract_canon_sha256":"278f3c78adae0f7a19e418d9b8867108dd2ff8879435442d386e6bde6336f308"},"schema_version":"1.0"},"canonical_sha256":"ebada0a1da5e0246d0be5cac31826ff9d1c0d19fe781787d8c984977a919944c","source":{"kind":"arxiv","id":"2402.05396","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.05396","created_at":"2026-07-05T09:39:21Z"},{"alias_kind":"arxiv_version","alias_value":"2402.05396v3","created_at":"2026-07-05T09:39:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.05396","created_at":"2026-07-05T09:39:21Z"},{"alias_kind":"pith_short_12","alias_value":"5OW2BIO2LYBE","created_at":"2026-07-05T09:39:21Z"},{"alias_kind":"pith_short_16","alias_value":"5OW2BIO2LYBENUF6","created_at":"2026-07-05T09:39:21Z"},{"alias_kind":"pith_short_8","alias_value":"5OW2BIO2","created_at":"2026-07-05T09:39:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:5OW2BIO2LYBENUF6LSWDDATP7H","target":"record","payload":{"canonical_record":{"source":{"id":"2402.05396","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-08T04:16:35Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"f5ac2eaec609b7784df018f7eb3c99c0df29b7c7d7f3cd1b6a32408229d7afe7","abstract_canon_sha256":"278f3c78adae0f7a19e418d9b8867108dd2ff8879435442d386e6bde6336f308"},"schema_version":"1.0"},"canonical_sha256":"ebada0a1da5e0246d0be5cac31826ff9d1c0d19fe781787d8c984977a919944c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:39:21.145003Z","signature_b64":"CwiuC41+qJtR06b5WzSwgUhAgCmzm53vjuBn3BzVX13cLKWIrO6+/BnlVL/AKswy7++Pe24xDYz1TxPyMPupBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ebada0a1da5e0246d0be5cac31826ff9d1c0d19fe781787d8c984977a919944c","last_reissued_at":"2026-07-05T09:39:21.144513Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:39:21.144513Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2402.05396","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:39:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Zhj2LabWtX+nbmVCIgoBT/3h9/Xx/Jt7WTPeNa4sxFx/py7apeedBIN02qXyKLSK8ci4N11Ex+sG5yFVsOV2Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T18:38:47.262119Z"},"content_sha256":"c88032de177e433729da88e1d493d7bbb816f45eb3ac8f6fd84a1dc7fb8393da","schema_version":"1.0","event_id":"sha256:c88032de177e433729da88e1d493d7bbb816f45eb3ac8f6fd84a1dc7fb8393da"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:5OW2BIO2LYBENUF6LSWDDATP7H","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"TASER: Temporal Adaptive Sampling for Fast and Accurate Dynamic Graph Representation Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Christopher Leung, Gangda Deng, Hanqing Zeng, Hongkuan Zhou, Jianbo Li, Rajgopal Kannan, Viktor Prasanna, Yinglong Xia","submitted_at":"2024-02-08T04:16:35Z","abstract_excerpt":"Recently, Temporal Graph Neural Networks (TGNNs) have demonstrated state-of-the-art performance in various high-impact applications, including fraud detection and content recommendation. Despite the success of TGNNs, they are prone to the prevalent noise found in real-world dynamic graphs like time-deprecated links and skewed interaction distribution. The noise causes two critical issues that significantly compromise the accuracy of TGNNs: (1) models are supervised by inferior interactions, and (2) noisy input induces high variance in the aggregated messages. However, current TGNN denoising te"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.05396","kind":"arxiv","version":3},"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/2402.05396/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":"2026-07-05T09:39:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8oWFhjI3PLZLgwfoBAAqloaaJpyiTBJN6D9+X7XkLyjJJOupdZClA0Z8n1rXKLDWlHBgmyLEr++1gfSD7HtICQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T18:38:47.263074Z"},"content_sha256":"0394d2d4eba1a15dc85644c761b26f3b470f634a5570a894c6f050410c22dd82","schema_version":"1.0","event_id":"sha256:0394d2d4eba1a15dc85644c761b26f3b470f634a5570a894c6f050410c22dd82"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5OW2BIO2LYBENUF6LSWDDATP7H/bundle.json","state_url":"https://pith.science/pith/5OW2BIO2LYBENUF6LSWDDATP7H/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5OW2BIO2LYBENUF6LSWDDATP7H/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":"2026-08-04T18:38:47Z","links":{"resolver":"https://pith.science/pith/5OW2BIO2LYBENUF6LSWDDATP7H","bundle":"https://pith.science/pith/5OW2BIO2LYBENUF6LSWDDATP7H/bundle.json","state":"https://pith.science/pith/5OW2BIO2LYBENUF6LSWDDATP7H/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5OW2BIO2LYBENUF6LSWDDATP7H/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:5OW2BIO2LYBENUF6LSWDDATP7H","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":"278f3c78adae0f7a19e418d9b8867108dd2ff8879435442d386e6bde6336f308","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-08T04:16:35Z","title_canon_sha256":"f5ac2eaec609b7784df018f7eb3c99c0df29b7c7d7f3cd1b6a32408229d7afe7"},"schema_version":"1.0","source":{"id":"2402.05396","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.05396","created_at":"2026-07-05T09:39:21Z"},{"alias_kind":"arxiv_version","alias_value":"2402.05396v3","created_at":"2026-07-05T09:39:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.05396","created_at":"2026-07-05T09:39:21Z"},{"alias_kind":"pith_short_12","alias_value":"5OW2BIO2LYBE","created_at":"2026-07-05T09:39:21Z"},{"alias_kind":"pith_short_16","alias_value":"5OW2BIO2LYBENUF6","created_at":"2026-07-05T09:39:21Z"},{"alias_kind":"pith_short_8","alias_value":"5OW2BIO2","created_at":"2026-07-05T09:39:21Z"}],"graph_snapshots":[{"event_id":"sha256:0394d2d4eba1a15dc85644c761b26f3b470f634a5570a894c6f050410c22dd82","target":"graph","created_at":"2026-07-05T09:39:21Z","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/2402.05396/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recently, Temporal Graph Neural Networks (TGNNs) have demonstrated state-of-the-art performance in various high-impact applications, including fraud detection and content recommendation. Despite the success of TGNNs, they are prone to the prevalent noise found in real-world dynamic graphs like time-deprecated links and skewed interaction distribution. The noise causes two critical issues that significantly compromise the accuracy of TGNNs: (1) models are supervised by inferior interactions, and (2) noisy input induces high variance in the aggregated messages. However, current TGNN denoising te","authors_text":"Christopher Leung, Gangda Deng, Hanqing Zeng, Hongkuan Zhou, Jianbo Li, Rajgopal Kannan, Viktor Prasanna, Yinglong Xia","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-08T04:16:35Z","title":"TASER: Temporal Adaptive Sampling for Fast and Accurate Dynamic Graph Representation Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.05396","kind":"arxiv","version":3},"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:c88032de177e433729da88e1d493d7bbb816f45eb3ac8f6fd84a1dc7fb8393da","target":"record","created_at":"2026-07-05T09:39:21Z","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":"278f3c78adae0f7a19e418d9b8867108dd2ff8879435442d386e6bde6336f308","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-08T04:16:35Z","title_canon_sha256":"f5ac2eaec609b7784df018f7eb3c99c0df29b7c7d7f3cd1b6a32408229d7afe7"},"schema_version":"1.0","source":{"id":"2402.05396","kind":"arxiv","version":3}},"canonical_sha256":"ebada0a1da5e0246d0be5cac31826ff9d1c0d19fe781787d8c984977a919944c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ebada0a1da5e0246d0be5cac31826ff9d1c0d19fe781787d8c984977a919944c","first_computed_at":"2026-07-05T09:39:21.144513Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:39:21.144513Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CwiuC41+qJtR06b5WzSwgUhAgCmzm53vjuBn3BzVX13cLKWIrO6+/BnlVL/AKswy7++Pe24xDYz1TxPyMPupBA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:39:21.145003Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.05396","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c88032de177e433729da88e1d493d7bbb816f45eb3ac8f6fd84a1dc7fb8393da","sha256:0394d2d4eba1a15dc85644c761b26f3b470f634a5570a894c6f050410c22dd82"],"state_sha256":"37b98806f0e72f25a71c2443471af61d68e154c96140fd28e92ddc487ee8f398"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nhogeXWv7upwvyK3pXU8aF3W7wm8Zerhg1dumOzx1LYFHgxJ32LgdMpUgOI6+ZUhYmuccvQ3rZMSTF1xQzSjDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T18:38:47.270574Z","bundle_sha256":"ff107627aa1f575212abcc1904793259a8203087c36038a340fded9e01799d03"}}