{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:EAMXRBAXIEJXNRWW2JECHSE454","short_pith_number":"pith:EAMXRBAX","schema_version":"1.0","canonical_sha256":"2019788417411376c6d6d24823c89cef057a9fe6fbf8a699ea8d052580f7f0e2","source":{"kind":"arxiv","id":"1910.07643","version":3},"attestation_state":"computed","paper":{"title":"Dynamic Graph Convolutional Networks Using the Tensor M-Product","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Haim Avron, Lior Horesh, Misha E. Kilmer, Osman Asif Malik, Shashanka Ubaru","submitted_at":"2019-10-16T23:06:34Z","abstract_excerpt":"Many irregular domains such as social networks, financial transactions, neuron connections, and natural language constructs are represented using graph structures. In recent years, a variety of graph neural networks (GNNs) have been successfully applied for representation learning and prediction on such graphs. In many of the real-world applications, the underlying graph changes over time, however, most of the existing GNNs are inadequate for handling such dynamic graphs. In this paper we propose a novel technique for learning embeddings of dynamic graphs using a tensor algebra framework. Our "},"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":"1910.07643","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-10-16T23:06:34Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"b42f69a5381ae0c4c22f1337e555333a97875b3748f94bc361a6aaf88d3fc0c7","abstract_canon_sha256":"13cf226605dc00786c0373b5729ee24055fc2cce37792b80971735e175f22d9c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:36:18.947714Z","signature_b64":"qam00UzrHIqyq/5Gxhwmu2L/FLEVi5gZE1SSM8X6Hgn9PWvXEOmQ1Jr7VDaOuageGf3YlRmF16bVijTe4ahKAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2019788417411376c6d6d24823c89cef057a9fe6fbf8a699ea8d052580f7f0e2","last_reissued_at":"2026-07-05T02:36:18.947263Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:36:18.947263Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dynamic Graph Convolutional Networks Using the Tensor M-Product","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Haim Avron, Lior Horesh, Misha E. Kilmer, Osman Asif Malik, Shashanka Ubaru","submitted_at":"2019-10-16T23:06:34Z","abstract_excerpt":"Many irregular domains such as social networks, financial transactions, neuron connections, and natural language constructs are represented using graph structures. In recent years, a variety of graph neural networks (GNNs) have been successfully applied for representation learning and prediction on such graphs. In many of the real-world applications, the underlying graph changes over time, however, most of the existing GNNs are inadequate for handling such dynamic graphs. In this paper we propose a novel technique for learning embeddings of dynamic graphs using a tensor algebra framework. Our "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.07643","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/1910.07643/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":"1910.07643","created_at":"2026-07-05T02:36:18.947322+00:00"},{"alias_kind":"arxiv_version","alias_value":"1910.07643v3","created_at":"2026-07-05T02:36:18.947322+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.07643","created_at":"2026-07-05T02:36:18.947322+00:00"},{"alias_kind":"pith_short_12","alias_value":"EAMXRBAXIEJX","created_at":"2026-07-05T02:36:18.947322+00:00"},{"alias_kind":"pith_short_16","alias_value":"EAMXRBAXIEJXNRWW","created_at":"2026-07-05T02:36:18.947322+00:00"},{"alias_kind":"pith_short_8","alias_value":"EAMXRBAX","created_at":"2026-07-05T02:36:18.947322+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/EAMXRBAXIEJXNRWW2JECHSE454","json":"https://pith.science/pith/EAMXRBAXIEJXNRWW2JECHSE454.json","graph_json":"https://pith.science/api/pith-number/EAMXRBAXIEJXNRWW2JECHSE454/graph.json","events_json":"https://pith.science/api/pith-number/EAMXRBAXIEJXNRWW2JECHSE454/events.json","paper":"https://pith.science/paper/EAMXRBAX"},"agent_actions":{"view_html":"https://pith.science/pith/EAMXRBAXIEJXNRWW2JECHSE454","download_json":"https://pith.science/pith/EAMXRBAXIEJXNRWW2JECHSE454.json","view_paper":"https://pith.science/paper/EAMXRBAX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1910.07643&json=true","fetch_graph":"https://pith.science/api/pith-number/EAMXRBAXIEJXNRWW2JECHSE454/graph.json","fetch_events":"https://pith.science/api/pith-number/EAMXRBAXIEJXNRWW2JECHSE454/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EAMXRBAXIEJXNRWW2JECHSE454/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EAMXRBAXIEJXNRWW2JECHSE454/action/storage_attestation","attest_author":"https://pith.science/pith/EAMXRBAXIEJXNRWW2JECHSE454/action/author_attestation","sign_citation":"https://pith.science/pith/EAMXRBAXIEJXNRWW2JECHSE454/action/citation_signature","submit_replication":"https://pith.science/pith/EAMXRBAXIEJXNRWW2JECHSE454/action/replication_record"}},"created_at":"2026-07-05T02:36:18.947322+00:00","updated_at":"2026-07-05T02:36:18.947322+00:00"}