{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:YOBY3T5Q5DODRPZSUH2L33PFGV","short_pith_number":"pith:YOBY3T5Q","schema_version":"1.0","canonical_sha256":"c3838dcfb0e8dc38bf32a1f4bdede5355ba1ae8e4e82eafc436c60d4e16e8e4d","source":{"kind":"arxiv","id":"2507.16541","version":1},"attestation_state":"computed","paper":{"title":"A Comprehensive Data-centric Overview of Federated Graph Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.SI"],"primary_cat":"cs.LG","authors_text":"Guochen Yan, Guoren Wang, Hao Zhang, Rong-Hua Li, Xinmo Jin, Xunkai Li, Yanyu Yan, Yinlin Zhu, Yuming Ai, Zekai Chen, Zhengyu Wu","submitted_at":"2025-07-22T12:49:24Z","abstract_excerpt":"In the era of big data applications, Federated Graph Learning (FGL) has emerged as a prominent solution that reconcile the tradeoff between optimizing the collective intelligence between decentralized datasets holders and preserving sensitive information to maximum. Existing FGL surveys have contributed meaningfully but largely focus on integrating Federated Learning (FL) and Graph Machine Learning (GML), resulting in early stage taxonomies that emphasis on methodology and simulated scenarios. Notably, a data centric perspective, which systematically examines FGL methods through the lens of da"},"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":"2507.16541","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-07-22T12:49:24Z","cross_cats_sorted":["cs.AI","cs.SI"],"title_canon_sha256":"fbd5d88b55cb7801f31622cf0f73fd8eff454207b8d156d82466ba5bb234ab89","abstract_canon_sha256":"03207fdde27e17fa4b7433f9585c3a807d28b1a657a387377ef480e6a9f5dccd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:41:11.751085Z","signature_b64":"KlmP+135c9uFWTgwfjYI2wxzb6uA4pVyOJFcoNbkkE7tQ4OcGxFTAw6HDr4s+QqgV3hYD2SrR6bP2mKbKEywBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c3838dcfb0e8dc38bf32a1f4bdede5355ba1ae8e4e82eafc436c60d4e16e8e4d","last_reissued_at":"2026-07-05T11:41:11.750605Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:41:11.750605Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Comprehensive Data-centric Overview of Federated Graph Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.SI"],"primary_cat":"cs.LG","authors_text":"Guochen Yan, Guoren Wang, Hao Zhang, Rong-Hua Li, Xinmo Jin, Xunkai Li, Yanyu Yan, Yinlin Zhu, Yuming Ai, Zekai Chen, Zhengyu Wu","submitted_at":"2025-07-22T12:49:24Z","abstract_excerpt":"In the era of big data applications, Federated Graph Learning (FGL) has emerged as a prominent solution that reconcile the tradeoff between optimizing the collective intelligence between decentralized datasets holders and preserving sensitive information to maximum. Existing FGL surveys have contributed meaningfully but largely focus on integrating Federated Learning (FL) and Graph Machine Learning (GML), resulting in early stage taxonomies that emphasis on methodology and simulated scenarios. Notably, a data centric perspective, which systematically examines FGL methods through the lens of da"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.16541","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/2507.16541/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":"2507.16541","created_at":"2026-07-05T11:41:11.750660+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.16541v1","created_at":"2026-07-05T11:41:11.750660+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.16541","created_at":"2026-07-05T11:41:11.750660+00:00"},{"alias_kind":"pith_short_12","alias_value":"YOBY3T5Q5DOD","created_at":"2026-07-05T11:41:11.750660+00:00"},{"alias_kind":"pith_short_16","alias_value":"YOBY3T5Q5DODRPZS","created_at":"2026-07-05T11:41:11.750660+00:00"},{"alias_kind":"pith_short_8","alias_value":"YOBY3T5Q","created_at":"2026-07-05T11:41:11.750660+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.11919","citing_title":"STAGE: Tackling Semantic Drift in Multimodal Federated Graph Learning","ref_index":2,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YOBY3T5Q5DODRPZSUH2L33PFGV","json":"https://pith.science/pith/YOBY3T5Q5DODRPZSUH2L33PFGV.json","graph_json":"https://pith.science/api/pith-number/YOBY3T5Q5DODRPZSUH2L33PFGV/graph.json","events_json":"https://pith.science/api/pith-number/YOBY3T5Q5DODRPZSUH2L33PFGV/events.json","paper":"https://pith.science/paper/YOBY3T5Q"},"agent_actions":{"view_html":"https://pith.science/pith/YOBY3T5Q5DODRPZSUH2L33PFGV","download_json":"https://pith.science/pith/YOBY3T5Q5DODRPZSUH2L33PFGV.json","view_paper":"https://pith.science/paper/YOBY3T5Q","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.16541&json=true","fetch_graph":"https://pith.science/api/pith-number/YOBY3T5Q5DODRPZSUH2L33PFGV/graph.json","fetch_events":"https://pith.science/api/pith-number/YOBY3T5Q5DODRPZSUH2L33PFGV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YOBY3T5Q5DODRPZSUH2L33PFGV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YOBY3T5Q5DODRPZSUH2L33PFGV/action/storage_attestation","attest_author":"https://pith.science/pith/YOBY3T5Q5DODRPZSUH2L33PFGV/action/author_attestation","sign_citation":"https://pith.science/pith/YOBY3T5Q5DODRPZSUH2L33PFGV/action/citation_signature","submit_replication":"https://pith.science/pith/YOBY3T5Q5DODRPZSUH2L33PFGV/action/replication_record"}},"created_at":"2026-07-05T11:41:11.750660+00:00","updated_at":"2026-07-05T11:41:11.750660+00:00"}