{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:L3UES7JCTDSBEF2IXHE3AREJPJ","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":"c23bb30b12b3f8b06892585b9c73d1990176e1787f925774866b0830a3e01fc6","cross_cats_sorted":["cs.DC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-01-25T23:16:03Z","title_canon_sha256":"6a4b5e51310bd7251d7b6320590c871e789e66ba32b4a867f0fa812d1e47c7b0"},"schema_version":"1.0","source":{"id":"2501.15348","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.15348","created_at":"2026-07-05T10:05:27Z"},{"alias_kind":"arxiv_version","alias_value":"2501.15348v1","created_at":"2026-07-05T10:05:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.15348","created_at":"2026-07-05T10:05:27Z"},{"alias_kind":"pith_short_12","alias_value":"L3UES7JCTDSB","created_at":"2026-07-05T10:05:27Z"},{"alias_kind":"pith_short_16","alias_value":"L3UES7JCTDSBEF2I","created_at":"2026-07-05T10:05:27Z"},{"alias_kind":"pith_short_8","alias_value":"L3UES7JC","created_at":"2026-07-05T10:05:27Z"}],"graph_snapshots":[{"event_id":"sha256:2523f35dc4d235bbdf519ed216da1d248802a09c5174bab7516227cd3ea2b3de","target":"graph","created_at":"2026-07-05T10:05:27Z","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/2501.15348/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Dynamic Graph Neural Networks (DGNNs) have gained widespread attention due to their applicability in diverse domains such as traffic network prediction, epidemiological forecasting, and social network analysis. In this paper, we present ReInc, a system designed to enable efficient and scalable training of DGNNs on large-scale graphs. ReInc introduces key innovations that capitalize on the unique combination of Graph Neural Networks (GNNs) and Recurrent Neural Networks (RNNs) inherent in DGNNs. By reusing intermediate results and incrementally computing aggregations across consecutive graph sna","authors_text":"Anand Padmanabha Iyer, Mingyu Guan, Saumia Singhal, Taesoo Kim","cross_cats":["cs.DC"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-01-25T23:16:03Z","title":"ReInc: Scaling Training of Dynamic Graph Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.15348","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":"sha256:4ac2a95b7d3b2c30295377bfe04987a1067e86a3df67d00f4811ddab89347e8a","target":"record","created_at":"2026-07-05T10:05:27Z","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":"c23bb30b12b3f8b06892585b9c73d1990176e1787f925774866b0830a3e01fc6","cross_cats_sorted":["cs.DC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-01-25T23:16:03Z","title_canon_sha256":"6a4b5e51310bd7251d7b6320590c871e789e66ba32b4a867f0fa812d1e47c7b0"},"schema_version":"1.0","source":{"id":"2501.15348","kind":"arxiv","version":1}},"canonical_sha256":"5ee8497d2298e4121748b9c9b044897a71dcc2519b4baa6c5f72f9066c56ba83","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5ee8497d2298e4121748b9c9b044897a71dcc2519b4baa6c5f72f9066c56ba83","first_computed_at":"2026-07-05T10:05:27.810955Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:05:27.810955Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"mJrB1bjF0zqArBK9Fc5fQADW2gMdcNrFwg99afU6rOt5XZj76LEwama8DS7G/G7XoGu1pxsjlK5CE7TRjgnBCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T10:05:27.811388Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.15348","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4ac2a95b7d3b2c30295377bfe04987a1067e86a3df67d00f4811ddab89347e8a","sha256:2523f35dc4d235bbdf519ed216da1d248802a09c5174bab7516227cd3ea2b3de"],"state_sha256":"d70151d56e697dd0333a454885ae993d05e86728dcaecf0d34ae75952eecc520"}