{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:P62633E4IPY52XHGFX3PXNT6NX","short_pith_number":"pith:P62633E4","schema_version":"1.0","canonical_sha256":"7fb5edec9c43f1dd5ce62df6fbb67e6df3799fbb66a0089084b207a3734a34dc","source":{"kind":"arxiv","id":"2605.25548","version":1},"attestation_state":"computed","paper":{"title":"'Si'multaneous 'S'patial-'T'emporal Message Passing for Dynamic Graph Representation Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Anirban Dasgupta, Shubhajit Roy","submitted_at":"2026-05-25T08:04:32Z","abstract_excerpt":"Dynamic graph neural networks (DGNNs) that operate on snapshot sequences typically fall into one of two categories. \\emph{Temporal-first} approaches build per-node temporal embeddings and only afterwards perform spatial aggregation, whereas \\emph{Spatial-first} approaches invert this order, feeding the output of a graph convolution into a downstream temporal module. In either case, the rigid sequencing forces the second stage to consume an already-compressed summary produced by the first, ruling out joint reasoning over topology and evolution; concretely, the message-passing operator never get"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2605.25548","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-25T08:04:32Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"4aa4e6e8050e1157895ffc3464c325f210596ed1485d143e7363f05753ea6993","abstract_canon_sha256":"e82b62dc9c01c852b32c541f7992bde849e8570ca3c6742f0440d504fb9b25b6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-26T02:04:42.202565Z","signature_b64":"jfkniOSP27pg+RUwCJT2ScGfl0oQvYNqdqIPU6Od3142jysgKhK/v2cvY9IDmmTwQIw/5g4YFX3k2pxk7thbAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7fb5edec9c43f1dd5ce62df6fbb67e6df3799fbb66a0089084b207a3734a34dc","last_reissued_at":"2026-05-26T02:04:42.201689Z","signature_status":"signed_v1","first_computed_at":"2026-05-26T02:04:42.201689Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"'Si'multaneous 'S'patial-'T'emporal Message Passing for Dynamic Graph Representation Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Anirban Dasgupta, Shubhajit Roy","submitted_at":"2026-05-25T08:04:32Z","abstract_excerpt":"Dynamic graph neural networks (DGNNs) that operate on snapshot sequences typically fall into one of two categories. \\emph{Temporal-first} approaches build per-node temporal embeddings and only afterwards perform spatial aggregation, whereas \\emph{Spatial-first} approaches invert this order, feeding the output of a graph convolution into a downstream temporal module. In either case, the rigid sequencing forces the second stage to consume an already-compressed summary produced by the first, ruling out joint reasoning over topology and evolution; concretely, the message-passing operator never get"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.25548","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/2605.25548/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2605.25548","created_at":"2026-05-26T02:04:42.201805+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.25548v1","created_at":"2026-05-26T02:04:42.201805+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.25548","created_at":"2026-05-26T02:04:42.201805+00:00"},{"alias_kind":"pith_short_12","alias_value":"P62633E4IPY5","created_at":"2026-05-26T02:04:42.201805+00:00"},{"alias_kind":"pith_short_16","alias_value":"P62633E4IPY52XHG","created_at":"2026-05-26T02:04:42.201805+00:00"},{"alias_kind":"pith_short_8","alias_value":"P62633E4","created_at":"2026-05-26T02:04:42.201805+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/P62633E4IPY52XHGFX3PXNT6NX","json":"https://pith.science/pith/P62633E4IPY52XHGFX3PXNT6NX.json","graph_json":"https://pith.science/api/pith-number/P62633E4IPY52XHGFX3PXNT6NX/graph.json","events_json":"https://pith.science/api/pith-number/P62633E4IPY52XHGFX3PXNT6NX/events.json","paper":"https://pith.science/paper/P62633E4"},"agent_actions":{"view_html":"https://pith.science/pith/P62633E4IPY52XHGFX3PXNT6NX","download_json":"https://pith.science/pith/P62633E4IPY52XHGFX3PXNT6NX.json","view_paper":"https://pith.science/paper/P62633E4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.25548&json=true","fetch_graph":"https://pith.science/api/pith-number/P62633E4IPY52XHGFX3PXNT6NX/graph.json","fetch_events":"https://pith.science/api/pith-number/P62633E4IPY52XHGFX3PXNT6NX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/P62633E4IPY52XHGFX3PXNT6NX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/P62633E4IPY52XHGFX3PXNT6NX/action/storage_attestation","attest_author":"https://pith.science/pith/P62633E4IPY52XHGFX3PXNT6NX/action/author_attestation","sign_citation":"https://pith.science/pith/P62633E4IPY52XHGFX3PXNT6NX/action/citation_signature","submit_replication":"https://pith.science/pith/P62633E4IPY52XHGFX3PXNT6NX/action/replication_record"}},"created_at":"2026-05-26T02:04:42.201805+00:00","updated_at":"2026-05-26T02:04:42.201805+00:00"}