{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:TV2VO6WHN2DI2UY4SBH74FO26A","short_pith_number":"pith:TV2VO6WH","schema_version":"1.0","canonical_sha256":"9d75577ac76e868d531c904ffe15daf030a1189c956baba8364c8c2a0fb1e303","source":{"kind":"arxiv","id":"2109.11898","version":1},"attestation_state":"computed","paper":{"title":"Graph Learning Augmented Heterogeneous Graph Neural Network for Social Recommendation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Bo Long, Ethan Chang, Fangli Xu, Lingfei Wu, Qi Shen, Yiming Zhang, Yitong Pang, Zhihua Wei","submitted_at":"2021-09-24T11:44:15Z","abstract_excerpt":"Social recommendation based on social network has achieved great success in improving the performance of recommendation system. Since social network (user-user relations) and user-item interactions are both naturally represented as graph-structured data, Graph Neural Networks (GNNs) have thus been widely applied for social recommendation. In this work, we propose an end-to-end heterogeneous global graph learning framework, namely Graph Learning Augmented Heterogeneous Graph Neural Network (GL-HGNN) for social recommendation. GL-HGNN aims to learn a heterogeneous global graph that makes full us"},"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":"2109.11898","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2021-09-24T11:44:15Z","cross_cats_sorted":[],"title_canon_sha256":"940d3c7709b9b9d8aeff39b406b8bc8fd8c7fccfca8818ac9e5a3d1803487941","abstract_canon_sha256":"699ccb0ad3346799fa2298604d8df6da7b49816352fc42c78f0024edc7aee33c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:17:08.636217Z","signature_b64":"jzylW5JFYEnXcNdq0P+2ia3XIT9XM2/XytQLzAlThaJz2ACAbPeQu24DDW5BF1dNU13tsHABc1DQ4XF5CkrZDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9d75577ac76e868d531c904ffe15daf030a1189c956baba8364c8c2a0fb1e303","last_reissued_at":"2026-07-05T03:17:08.635801Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:17:08.635801Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Graph Learning Augmented Heterogeneous Graph Neural Network for Social Recommendation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Bo Long, Ethan Chang, Fangli Xu, Lingfei Wu, Qi Shen, Yiming Zhang, Yitong Pang, Zhihua Wei","submitted_at":"2021-09-24T11:44:15Z","abstract_excerpt":"Social recommendation based on social network has achieved great success in improving the performance of recommendation system. Since social network (user-user relations) and user-item interactions are both naturally represented as graph-structured data, Graph Neural Networks (GNNs) have thus been widely applied for social recommendation. In this work, we propose an end-to-end heterogeneous global graph learning framework, namely Graph Learning Augmented Heterogeneous Graph Neural Network (GL-HGNN) for social recommendation. GL-HGNN aims to learn a heterogeneous global graph that makes full us"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.11898","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/2109.11898/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":"2109.11898","created_at":"2026-07-05T03:17:08.635862+00:00"},{"alias_kind":"arxiv_version","alias_value":"2109.11898v1","created_at":"2026-07-05T03:17:08.635862+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.11898","created_at":"2026-07-05T03:17:08.635862+00:00"},{"alias_kind":"pith_short_12","alias_value":"TV2VO6WHN2DI","created_at":"2026-07-05T03:17:08.635862+00:00"},{"alias_kind":"pith_short_16","alias_value":"TV2VO6WHN2DI2UY4","created_at":"2026-07-05T03:17:08.635862+00:00"},{"alias_kind":"pith_short_8","alias_value":"TV2VO6WH","created_at":"2026-07-05T03:17:08.635862+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/TV2VO6WHN2DI2UY4SBH74FO26A","json":"https://pith.science/pith/TV2VO6WHN2DI2UY4SBH74FO26A.json","graph_json":"https://pith.science/api/pith-number/TV2VO6WHN2DI2UY4SBH74FO26A/graph.json","events_json":"https://pith.science/api/pith-number/TV2VO6WHN2DI2UY4SBH74FO26A/events.json","paper":"https://pith.science/paper/TV2VO6WH"},"agent_actions":{"view_html":"https://pith.science/pith/TV2VO6WHN2DI2UY4SBH74FO26A","download_json":"https://pith.science/pith/TV2VO6WHN2DI2UY4SBH74FO26A.json","view_paper":"https://pith.science/paper/TV2VO6WH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2109.11898&json=true","fetch_graph":"https://pith.science/api/pith-number/TV2VO6WHN2DI2UY4SBH74FO26A/graph.json","fetch_events":"https://pith.science/api/pith-number/TV2VO6WHN2DI2UY4SBH74FO26A/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TV2VO6WHN2DI2UY4SBH74FO26A/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TV2VO6WHN2DI2UY4SBH74FO26A/action/storage_attestation","attest_author":"https://pith.science/pith/TV2VO6WHN2DI2UY4SBH74FO26A/action/author_attestation","sign_citation":"https://pith.science/pith/TV2VO6WHN2DI2UY4SBH74FO26A/action/citation_signature","submit_replication":"https://pith.science/pith/TV2VO6WHN2DI2UY4SBH74FO26A/action/replication_record"}},"created_at":"2026-07-05T03:17:08.635862+00:00","updated_at":"2026-07-05T03:17:08.635862+00:00"}