{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:BER7POXZFZMPXLOU46YDPEDRGN","short_pith_number":"pith:BER7POXZ","schema_version":"1.0","canonical_sha256":"0923f7baf92e58fbadd4e7b0379071337e26d7cb15ce977b12f5e3a959056c67","source":{"kind":"arxiv","id":"1902.11054","version":2},"attestation_state":"computed","paper":{"title":"Link Prediction with Mutual Attention for Text-Attributed Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.SI"],"primary_cat":"cs.CL","authors_text":"Adrien Guille, Julien Velcin, Robin Brochier","submitted_at":"2019-02-28T12:45:42Z","abstract_excerpt":"In this extended abstract, we present an algorithm that learns a similarity measure between documents from the network topology of a structured corpus. We leverage the Scaled Dot-Product Attention, a recently proposed attention mechanism, to design a mutual attention mechanism between pairs of documents. To train its parameters, we use the network links as supervision. We provide preliminary experiment results with a citation dataset on two prediction tasks, demonstrating the capacity of our model to learn a meaningful textual similarity."},"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":"1902.11054","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2019-02-28T12:45:42Z","cross_cats_sorted":["cs.LG","cs.SI"],"title_canon_sha256":"57e1afe0815aeac8b2ec46ad0bd74140e3df367a7f771d30324e8b2c27030c0d","abstract_canon_sha256":"676fa4e911fadfaa035fd7361e24bfbd3548749ac1493baba9cefa77fa3dc5af"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:50:48.535336Z","signature_b64":"tQXSZIEeP3wGqkHk/7bfCfyPnmFyqXMHJ4EbNGgBknnoKqEnaeWrfp/jcSmcUDs5e7VrDPN9zAcbPbqcYBAbAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0923f7baf92e58fbadd4e7b0379071337e26d7cb15ce977b12f5e3a959056c67","last_reissued_at":"2026-05-17T23:50:48.534771Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:50:48.534771Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Link Prediction with Mutual Attention for Text-Attributed Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.SI"],"primary_cat":"cs.CL","authors_text":"Adrien Guille, Julien Velcin, Robin Brochier","submitted_at":"2019-02-28T12:45:42Z","abstract_excerpt":"In this extended abstract, we present an algorithm that learns a similarity measure between documents from the network topology of a structured corpus. We leverage the Scaled Dot-Product Attention, a recently proposed attention mechanism, to design a mutual attention mechanism between pairs of documents. To train its parameters, we use the network links as supervision. We provide preliminary experiment results with a citation dataset on two prediction tasks, demonstrating the capacity of our model to learn a meaningful textual similarity."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.11054","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1902.11054","created_at":"2026-05-17T23:50:48.534850+00:00"},{"alias_kind":"arxiv_version","alias_value":"1902.11054v2","created_at":"2026-05-17T23:50:48.534850+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.11054","created_at":"2026-05-17T23:50:48.534850+00:00"},{"alias_kind":"pith_short_12","alias_value":"BER7POXZFZMP","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_16","alias_value":"BER7POXZFZMPXLOU","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_8","alias_value":"BER7POXZ","created_at":"2026-05-18T12:33:12.712433+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/BER7POXZFZMPXLOU46YDPEDRGN","json":"https://pith.science/pith/BER7POXZFZMPXLOU46YDPEDRGN.json","graph_json":"https://pith.science/api/pith-number/BER7POXZFZMPXLOU46YDPEDRGN/graph.json","events_json":"https://pith.science/api/pith-number/BER7POXZFZMPXLOU46YDPEDRGN/events.json","paper":"https://pith.science/paper/BER7POXZ"},"agent_actions":{"view_html":"https://pith.science/pith/BER7POXZFZMPXLOU46YDPEDRGN","download_json":"https://pith.science/pith/BER7POXZFZMPXLOU46YDPEDRGN.json","view_paper":"https://pith.science/paper/BER7POXZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1902.11054&json=true","fetch_graph":"https://pith.science/api/pith-number/BER7POXZFZMPXLOU46YDPEDRGN/graph.json","fetch_events":"https://pith.science/api/pith-number/BER7POXZFZMPXLOU46YDPEDRGN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BER7POXZFZMPXLOU46YDPEDRGN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BER7POXZFZMPXLOU46YDPEDRGN/action/storage_attestation","attest_author":"https://pith.science/pith/BER7POXZFZMPXLOU46YDPEDRGN/action/author_attestation","sign_citation":"https://pith.science/pith/BER7POXZFZMPXLOU46YDPEDRGN/action/citation_signature","submit_replication":"https://pith.science/pith/BER7POXZFZMPXLOU46YDPEDRGN/action/replication_record"}},"created_at":"2026-05-17T23:50:48.534850+00:00","updated_at":"2026-05-17T23:50:48.534850+00:00"}