{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:JAVFFR32VRCSZ4NLKSDC55KNMA","short_pith_number":"pith:JAVFFR32","schema_version":"1.0","canonical_sha256":"482a52c77aac452cf1ab54862ef54d602b6b453d0744f1194bcfda91b102f7ea","source":{"kind":"arxiv","id":"2201.08802","version":3},"attestation_state":"computed","paper":{"title":"Deconfounding to Explanation Evaluation in Graph Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"An Zhang, Fuli Feng, Tat-Seng Chua, Xia Hu, Xiangnan He, Xiang Wang, Ying-Xin Wu","submitted_at":"2022-01-21T18:05:00Z","abstract_excerpt":"Explainability of graph neural networks (GNNs) aims to answer \"Why the GNN made a certain prediction?\", which is crucial to interpret the model prediction. The feature attribution framework distributes a GNN's prediction to its input features (e.g., edges), identifying an influential subgraph as the explanation. When evaluating the explanation (i.e., subgraph importance), a standard way is to audit the model prediction based on the subgraph solely. However, we argue that a distribution shift exists between the full graph and the subgraph, causing the out-of-distribution problem. Furthermore, w"},"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":"2201.08802","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-01-21T18:05:00Z","cross_cats_sorted":[],"title_canon_sha256":"e3575348be054ea411fd8229b5b48582fbd9836f7dd3c796faa9292d7f26087e","abstract_canon_sha256":"8bbacf74189ca566aac687996908e01429e537537d6ee4a523e1f362e4c58fd3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:53:16.891884Z","signature_b64":"jD0nQKmsl52Iw82QOfSvC9BLmES9eP1/Z1qLP6rozd/tRuWdyEiHuBfgaSugVK7yAhyvV/G4n0p9uJSICV8fBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"482a52c77aac452cf1ab54862ef54d602b6b453d0744f1194bcfda91b102f7ea","last_reissued_at":"2026-07-05T03:53:16.891489Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:53:16.891489Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deconfounding to Explanation Evaluation in Graph Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"An Zhang, Fuli Feng, Tat-Seng Chua, Xia Hu, Xiangnan He, Xiang Wang, Ying-Xin Wu","submitted_at":"2022-01-21T18:05:00Z","abstract_excerpt":"Explainability of graph neural networks (GNNs) aims to answer \"Why the GNN made a certain prediction?\", which is crucial to interpret the model prediction. The feature attribution framework distributes a GNN's prediction to its input features (e.g., edges), identifying an influential subgraph as the explanation. When evaluating the explanation (i.e., subgraph importance), a standard way is to audit the model prediction based on the subgraph solely. However, we argue that a distribution shift exists between the full graph and the subgraph, causing the out-of-distribution problem. Furthermore, w"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2201.08802","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/2201.08802/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":"2201.08802","created_at":"2026-07-05T03:53:16.891548+00:00"},{"alias_kind":"arxiv_version","alias_value":"2201.08802v3","created_at":"2026-07-05T03:53:16.891548+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2201.08802","created_at":"2026-07-05T03:53:16.891548+00:00"},{"alias_kind":"pith_short_12","alias_value":"JAVFFR32VRCS","created_at":"2026-07-05T03:53:16.891548+00:00"},{"alias_kind":"pith_short_16","alias_value":"JAVFFR32VRCSZ4NL","created_at":"2026-07-05T03:53:16.891548+00:00"},{"alias_kind":"pith_short_8","alias_value":"JAVFFR32","created_at":"2026-07-05T03:53:16.891548+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.00440","citing_title":"A Recipe for Causal Graph Regression: Confounding Effects Revisited","ref_index":49,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JAVFFR32VRCSZ4NLKSDC55KNMA","json":"https://pith.science/pith/JAVFFR32VRCSZ4NLKSDC55KNMA.json","graph_json":"https://pith.science/api/pith-number/JAVFFR32VRCSZ4NLKSDC55KNMA/graph.json","events_json":"https://pith.science/api/pith-number/JAVFFR32VRCSZ4NLKSDC55KNMA/events.json","paper":"https://pith.science/paper/JAVFFR32"},"agent_actions":{"view_html":"https://pith.science/pith/JAVFFR32VRCSZ4NLKSDC55KNMA","download_json":"https://pith.science/pith/JAVFFR32VRCSZ4NLKSDC55KNMA.json","view_paper":"https://pith.science/paper/JAVFFR32","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2201.08802&json=true","fetch_graph":"https://pith.science/api/pith-number/JAVFFR32VRCSZ4NLKSDC55KNMA/graph.json","fetch_events":"https://pith.science/api/pith-number/JAVFFR32VRCSZ4NLKSDC55KNMA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JAVFFR32VRCSZ4NLKSDC55KNMA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JAVFFR32VRCSZ4NLKSDC55KNMA/action/storage_attestation","attest_author":"https://pith.science/pith/JAVFFR32VRCSZ4NLKSDC55KNMA/action/author_attestation","sign_citation":"https://pith.science/pith/JAVFFR32VRCSZ4NLKSDC55KNMA/action/citation_signature","submit_replication":"https://pith.science/pith/JAVFFR32VRCSZ4NLKSDC55KNMA/action/replication_record"}},"created_at":"2026-07-05T03:53:16.891548+00:00","updated_at":"2026-07-05T03:53:16.891548+00:00"}