{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:5S3EEUJC6XD3CI354XVG55TBHE","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":"c074bcc8c3d85e61a06b372a95ab10d7319c643a6042b0d9efb942f033e38a4e","cross_cats_sorted":["cs.NE","q-fin.CP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-12-05T10:15:56Z","title_canon_sha256":"3ba6cd7d109492eed135dd675358e94b095539ca898f50fe06d71f5282208799"},"schema_version":"1.0","source":{"id":"2412.04034","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.04034","created_at":"2026-07-05T10:22:10Z"},{"alias_kind":"arxiv_version","alias_value":"2412.04034v1","created_at":"2026-07-05T10:22:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.04034","created_at":"2026-07-05T10:22:10Z"},{"alias_kind":"pith_short_12","alias_value":"5S3EEUJC6XD3","created_at":"2026-07-05T10:22:10Z"},{"alias_kind":"pith_short_16","alias_value":"5S3EEUJC6XD3CI35","created_at":"2026-07-05T10:22:10Z"},{"alias_kind":"pith_short_8","alias_value":"5S3EEUJC","created_at":"2026-07-05T10:22:10Z"}],"graph_snapshots":[{"event_id":"sha256:79c35d4bee23e34131d55c318355ed8ea1f992590e8e1e4594ffb317070d0269","target":"graph","created_at":"2026-07-05T10:22:10Z","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/2412.04034/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Temporal Graph Learning (TGL) is crucial for capturing the evolving nature of stock markets. Traditional methods often ignore the interplay between dynamic temporal changes and static relational structures between stocks. To address this issue, we propose the Dynamic Graph Representation with Contrastive Learning (DGRCL) framework, which integrates dynamic and static graph relations to improve the accuracy of stock trend prediction. Our framework introduces two key components: the Embedding Enhancement (EE) module and the Contrastive Constrained Training (CCT) module. The EE module focuses on ","authors_text":"Jin Zheng, John Cartlidge, Yunhua Pei","cross_cats":["cs.NE","q-fin.CP"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-12-05T10:15:56Z","title":"Dynamic Graph Representation with Contrastive Learning for Financial Market Prediction: Integrating Temporal Evolution and Static Relations"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.04034","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":"sha256:3ebb290684ec01240c2e18f603b730d9482a5551f0f7b10a786d1ca2fffbc2a7","target":"record","created_at":"2026-07-05T10:22:10Z","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":"c074bcc8c3d85e61a06b372a95ab10d7319c643a6042b0d9efb942f033e38a4e","cross_cats_sorted":["cs.NE","q-fin.CP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-12-05T10:15:56Z","title_canon_sha256":"3ba6cd7d109492eed135dd675358e94b095539ca898f50fe06d71f5282208799"},"schema_version":"1.0","source":{"id":"2412.04034","kind":"arxiv","version":1}},"canonical_sha256":"ecb6425122f5c7b1237de5ea6ef661392f1c6c7a93c3fb02b229001d94b69339","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ecb6425122f5c7b1237de5ea6ef661392f1c6c7a93c3fb02b229001d94b69339","first_computed_at":"2026-07-05T10:22:10.886209Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:22:10.886209Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ORFlKzQ+y6uBYRRbWVVo0v3/DdX3XSUj85cyCYf4bY4QcS8UEADedsly6cn4REJf5b3pRO/gnzcEhMekVnNVCw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:22:10.886759Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.04034","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3ebb290684ec01240c2e18f603b730d9482a5551f0f7b10a786d1ca2fffbc2a7","sha256:79c35d4bee23e34131d55c318355ed8ea1f992590e8e1e4594ffb317070d0269"],"state_sha256":"cc6c97dd93e13d8efce05a7a6d966b1a58f1e5cf24452ff37deadfed869b695c"}