{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:VALEX6DPDKANGJBRWMHM46QHBN","short_pith_number":"pith:VALEX6DP","schema_version":"1.0","canonical_sha256":"a8164bf86f1a80d32431b30ece7a070b558c513cff403615d6855544f9b11926","source":{"kind":"arxiv","id":"2208.06129","version":1},"attestation_state":"computed","paper":{"title":"Multiplex Heterogeneous Graph Convolutional Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.SI","authors_text":"Chaofan Fu, Chao Huang, Junyu Dong, Pengyang Yu, Yanwei Yu, Zhongying Zhao","submitted_at":"2022-08-12T06:17:54Z","abstract_excerpt":"Heterogeneous graph convolutional networks have gained great popularity in tackling various network analytical tasks on heterogeneous network data, ranging from link prediction to node classification. However, most existing works ignore the relation heterogeneity with multiplex network between multi-typed nodes and different importance of relations in meta-paths for node embedding, which can hardly capture the heterogeneous structure signals across different relations. To tackle this challenge, this work proposes a Multiplex Heterogeneous Graph Convolutional Network (MHGCN) for heterogeneous n"},"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":"2208.06129","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2022-08-12T06:17:54Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"a97500ffa067e22735dfc5caa2c692b221cc3b8174d1b5770f407b7d1eb8af48","abstract_canon_sha256":"33f2d578049798353372a8e830f1503a7339fe56c68c18883cb105335117669f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:48:01.522428Z","signature_b64":"aAD2WCOVECfG0BwQEB+TTNd8cJjDvhg7MTpo4pFHMn3biiHOgvutlcHycMdN1w15VGy1MSuJ0faqictVp+ooDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a8164bf86f1a80d32431b30ece7a070b558c513cff403615d6855544f9b11926","last_reissued_at":"2026-07-05T04:48:01.521999Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:48:01.521999Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multiplex Heterogeneous Graph Convolutional Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.SI","authors_text":"Chaofan Fu, Chao Huang, Junyu Dong, Pengyang Yu, Yanwei Yu, Zhongying Zhao","submitted_at":"2022-08-12T06:17:54Z","abstract_excerpt":"Heterogeneous graph convolutional networks have gained great popularity in tackling various network analytical tasks on heterogeneous network data, ranging from link prediction to node classification. However, most existing works ignore the relation heterogeneity with multiplex network between multi-typed nodes and different importance of relations in meta-paths for node embedding, which can hardly capture the heterogeneous structure signals across different relations. To tackle this challenge, this work proposes a Multiplex Heterogeneous Graph Convolutional Network (MHGCN) for heterogeneous n"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.06129","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/2208.06129/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":"2208.06129","created_at":"2026-07-05T04:48:01.522056+00:00"},{"alias_kind":"arxiv_version","alias_value":"2208.06129v1","created_at":"2026-07-05T04:48:01.522056+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.06129","created_at":"2026-07-05T04:48:01.522056+00:00"},{"alias_kind":"pith_short_12","alias_value":"VALEX6DPDKAN","created_at":"2026-07-05T04:48:01.522056+00:00"},{"alias_kind":"pith_short_16","alias_value":"VALEX6DPDKANGJBR","created_at":"2026-07-05T04:48:01.522056+00:00"},{"alias_kind":"pith_short_8","alias_value":"VALEX6DP","created_at":"2026-07-05T04:48:01.522056+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/VALEX6DPDKANGJBRWMHM46QHBN","json":"https://pith.science/pith/VALEX6DPDKANGJBRWMHM46QHBN.json","graph_json":"https://pith.science/api/pith-number/VALEX6DPDKANGJBRWMHM46QHBN/graph.json","events_json":"https://pith.science/api/pith-number/VALEX6DPDKANGJBRWMHM46QHBN/events.json","paper":"https://pith.science/paper/VALEX6DP"},"agent_actions":{"view_html":"https://pith.science/pith/VALEX6DPDKANGJBRWMHM46QHBN","download_json":"https://pith.science/pith/VALEX6DPDKANGJBRWMHM46QHBN.json","view_paper":"https://pith.science/paper/VALEX6DP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2208.06129&json=true","fetch_graph":"https://pith.science/api/pith-number/VALEX6DPDKANGJBRWMHM46QHBN/graph.json","fetch_events":"https://pith.science/api/pith-number/VALEX6DPDKANGJBRWMHM46QHBN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VALEX6DPDKANGJBRWMHM46QHBN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VALEX6DPDKANGJBRWMHM46QHBN/action/storage_attestation","attest_author":"https://pith.science/pith/VALEX6DPDKANGJBRWMHM46QHBN/action/author_attestation","sign_citation":"https://pith.science/pith/VALEX6DPDKANGJBRWMHM46QHBN/action/citation_signature","submit_replication":"https://pith.science/pith/VALEX6DPDKANGJBRWMHM46QHBN/action/replication_record"}},"created_at":"2026-07-05T04:48:01.522056+00:00","updated_at":"2026-07-05T04:48:01.522056+00:00"}