{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:SQDEEQCBWVWMBCUYZEDIGEYJRV","short_pith_number":"pith:SQDEEQCB","schema_version":"1.0","canonical_sha256":"9406424041b56cc08a98c9068313098d49c4d40890e572af66efb6922d1e84bf","source":{"kind":"arxiv","id":"2304.01391","version":3},"attestation_state":"computed","paper":{"title":"Counterfactual Learning on Graphs: A Survey","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Charu Aggarwal, Hui Liu, Suhang Wang, Teng Xiao, Zhimeng Guo, Zongyu Wu","submitted_at":"2023-04-03T21:42:42Z","abstract_excerpt":"Graph-structured data are pervasive in the real-world such as social networks, molecular graphs and transaction networks. Graph neural networks (GNNs) have achieved great success in representation learning on graphs, facilitating various downstream tasks. However, GNNs have several drawbacks such as lacking interpretability, can easily inherit the bias of data and cannot model casual relations. Recently, counterfactual learning on graphs has shown promising results in alleviating these drawbacks. Various approaches have been proposed for counterfactual fairness, explainability, link prediction"},"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":"2304.01391","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-04-03T21:42:42Z","cross_cats_sorted":[],"title_canon_sha256":"d8f51ee8b2387b18f4bf0d4f55bdf88dd29b57761a4b6b4cfd26ab9f6ce55803","abstract_canon_sha256":"ab17d8a64722048d529c49cecb094a3c10cf892d41180fd70754966b572e9eb8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:19:54.462344Z","signature_b64":"Yo1gfaxaGoeNJd98a9aU46ds0gDa6erKBGwQfcWDjY9JqRg4tbcXyPj4tSQ8i7GffIu/j3BYVrYS94B41gTPBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9406424041b56cc08a98c9068313098d49c4d40890e572af66efb6922d1e84bf","last_reissued_at":"2026-07-05T10:19:54.461846Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:19:54.461846Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Counterfactual Learning on Graphs: A Survey","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Charu Aggarwal, Hui Liu, Suhang Wang, Teng Xiao, Zhimeng Guo, Zongyu Wu","submitted_at":"2023-04-03T21:42:42Z","abstract_excerpt":"Graph-structured data are pervasive in the real-world such as social networks, molecular graphs and transaction networks. Graph neural networks (GNNs) have achieved great success in representation learning on graphs, facilitating various downstream tasks. However, GNNs have several drawbacks such as lacking interpretability, can easily inherit the bias of data and cannot model casual relations. Recently, counterfactual learning on graphs has shown promising results in alleviating these drawbacks. Various approaches have been proposed for counterfactual fairness, explainability, link prediction"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.01391","kind":"arxiv","version":3},"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/2304.01391/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":"2304.01391","created_at":"2026-07-05T10:19:54.461901+00:00"},{"alias_kind":"arxiv_version","alias_value":"2304.01391v3","created_at":"2026-07-05T10:19:54.461901+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.01391","created_at":"2026-07-05T10:19:54.461901+00:00"},{"alias_kind":"pith_short_12","alias_value":"SQDEEQCBWVWM","created_at":"2026-07-05T10:19:54.461901+00:00"},{"alias_kind":"pith_short_16","alias_value":"SQDEEQCBWVWMBCUY","created_at":"2026-07-05T10:19:54.461901+00:00"},{"alias_kind":"pith_short_8","alias_value":"SQDEEQCB","created_at":"2026-07-05T10:19:54.461901+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/SQDEEQCBWVWMBCUYZEDIGEYJRV","json":"https://pith.science/pith/SQDEEQCBWVWMBCUYZEDIGEYJRV.json","graph_json":"https://pith.science/api/pith-number/SQDEEQCBWVWMBCUYZEDIGEYJRV/graph.json","events_json":"https://pith.science/api/pith-number/SQDEEQCBWVWMBCUYZEDIGEYJRV/events.json","paper":"https://pith.science/paper/SQDEEQCB"},"agent_actions":{"view_html":"https://pith.science/pith/SQDEEQCBWVWMBCUYZEDIGEYJRV","download_json":"https://pith.science/pith/SQDEEQCBWVWMBCUYZEDIGEYJRV.json","view_paper":"https://pith.science/paper/SQDEEQCB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2304.01391&json=true","fetch_graph":"https://pith.science/api/pith-number/SQDEEQCBWVWMBCUYZEDIGEYJRV/graph.json","fetch_events":"https://pith.science/api/pith-number/SQDEEQCBWVWMBCUYZEDIGEYJRV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SQDEEQCBWVWMBCUYZEDIGEYJRV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SQDEEQCBWVWMBCUYZEDIGEYJRV/action/storage_attestation","attest_author":"https://pith.science/pith/SQDEEQCBWVWMBCUYZEDIGEYJRV/action/author_attestation","sign_citation":"https://pith.science/pith/SQDEEQCBWVWMBCUYZEDIGEYJRV/action/citation_signature","submit_replication":"https://pith.science/pith/SQDEEQCBWVWMBCUYZEDIGEYJRV/action/replication_record"}},"created_at":"2026-07-05T10:19:54.461901+00:00","updated_at":"2026-07-05T10:19:54.461901+00:00"}