{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:ICXN6GNDHZ5QQUX2YHPQVMFM7N","short_pith_number":"pith:ICXN6GND","schema_version":"1.0","canonical_sha256":"40aedf19a33e7b0852fac1df0ab0acfb41158ba44dda399bff9a87d9b5a87431","source":{"kind":"arxiv","id":"2011.12683","version":2},"attestation_state":"computed","paper":{"title":"GraphHINGE: Learning Interaction Models of Structured Neighborhood on Heterogeneous Information Network","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Alexander J. Smola, Jiarui Jin, Jiarui Qin, Kounianhua Du, Weinan Zhang, Yong Yu, Yuchen Fang, Zheng Zhang","submitted_at":"2020-11-25T12:30:04Z","abstract_excerpt":"Heterogeneous information network (HIN) has been widely used to characterize entities of various types and their complex relations. Recent attempts either rely on explicit path reachability to leverage path-based semantic relatedness or graph neighborhood to learn heterogeneous network representations before predictions. These weakly coupled manners overlook the rich interactions among neighbor nodes, which introduces an early summarization issue. In this paper, we propose GraphHINGE (Heterogeneous INteract and aggreGatE), which captures and aggregates the interactive patterns between each pai"},"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":"2011.12683","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.IR","submitted_at":"2020-11-25T12:30:04Z","cross_cats_sorted":[],"title_canon_sha256":"018da8e6767c2812064653a2ec24442938b18550963a370a60f450b5a1292b32","abstract_canon_sha256":"a130501903d64043950e4bf9de13b09fa269c3c0509d81b729ec178e22100706"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:54:15.410605Z","signature_b64":"zC4sUTPb4lUtVlLVMmT2tTdXyKvDr24lcTyucQcvI9Wkzktqz2DkfENfpBWKRTgtKmF38CB0EuZ9yPaG+jFfCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"40aedf19a33e7b0852fac1df0ab0acfb41158ba44dda399bff9a87d9b5a87431","last_reissued_at":"2026-07-05T02:54:15.410103Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:54:15.410103Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GraphHINGE: Learning Interaction Models of Structured Neighborhood on Heterogeneous Information Network","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Alexander J. Smola, Jiarui Jin, Jiarui Qin, Kounianhua Du, Weinan Zhang, Yong Yu, Yuchen Fang, Zheng Zhang","submitted_at":"2020-11-25T12:30:04Z","abstract_excerpt":"Heterogeneous information network (HIN) has been widely used to characterize entities of various types and their complex relations. Recent attempts either rely on explicit path reachability to leverage path-based semantic relatedness or graph neighborhood to learn heterogeneous network representations before predictions. These weakly coupled manners overlook the rich interactions among neighbor nodes, which introduces an early summarization issue. In this paper, we propose GraphHINGE (Heterogeneous INteract and aggreGatE), which captures and aggregates the interactive patterns between each pai"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2011.12683","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2011.12683/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":"2011.12683","created_at":"2026-07-05T02:54:15.410162+00:00"},{"alias_kind":"arxiv_version","alias_value":"2011.12683v2","created_at":"2026-07-05T02:54:15.410162+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2011.12683","created_at":"2026-07-05T02:54:15.410162+00:00"},{"alias_kind":"pith_short_12","alias_value":"ICXN6GNDHZ5Q","created_at":"2026-07-05T02:54:15.410162+00:00"},{"alias_kind":"pith_short_16","alias_value":"ICXN6GNDHZ5QQUX2","created_at":"2026-07-05T02:54:15.410162+00:00"},{"alias_kind":"pith_short_8","alias_value":"ICXN6GND","created_at":"2026-07-05T02:54:15.410162+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/ICXN6GNDHZ5QQUX2YHPQVMFM7N","json":"https://pith.science/pith/ICXN6GNDHZ5QQUX2YHPQVMFM7N.json","graph_json":"https://pith.science/api/pith-number/ICXN6GNDHZ5QQUX2YHPQVMFM7N/graph.json","events_json":"https://pith.science/api/pith-number/ICXN6GNDHZ5QQUX2YHPQVMFM7N/events.json","paper":"https://pith.science/paper/ICXN6GND"},"agent_actions":{"view_html":"https://pith.science/pith/ICXN6GNDHZ5QQUX2YHPQVMFM7N","download_json":"https://pith.science/pith/ICXN6GNDHZ5QQUX2YHPQVMFM7N.json","view_paper":"https://pith.science/paper/ICXN6GND","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2011.12683&json=true","fetch_graph":"https://pith.science/api/pith-number/ICXN6GNDHZ5QQUX2YHPQVMFM7N/graph.json","fetch_events":"https://pith.science/api/pith-number/ICXN6GNDHZ5QQUX2YHPQVMFM7N/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ICXN6GNDHZ5QQUX2YHPQVMFM7N/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ICXN6GNDHZ5QQUX2YHPQVMFM7N/action/storage_attestation","attest_author":"https://pith.science/pith/ICXN6GNDHZ5QQUX2YHPQVMFM7N/action/author_attestation","sign_citation":"https://pith.science/pith/ICXN6GNDHZ5QQUX2YHPQVMFM7N/action/citation_signature","submit_replication":"https://pith.science/pith/ICXN6GNDHZ5QQUX2YHPQVMFM7N/action/replication_record"}},"created_at":"2026-07-05T02:54:15.410162+00:00","updated_at":"2026-07-05T02:54:15.410162+00:00"}