{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:KSYHQX2GSV3MSXKM7T2FHWQ2EZ","short_pith_number":"pith:KSYHQX2G","schema_version":"1.0","canonical_sha256":"54b0785f469576c95d4cfcf453da1a264c5f31abc078c1ce5bbfe29347087b9a","source":{"kind":"arxiv","id":"2306.15902","version":1},"attestation_state":"computed","paper":{"title":"Individual and Structural Graph Information Bottlenecks for Out-of-Distribution Generalization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Bin Cui, Heyuan Wang, Jiayi Zheng, Ling Yang, Shenda Hong, Wentao Zhang, Zhilin Huang, Zhongyi Liu","submitted_at":"2023-06-28T03:52:41Z","abstract_excerpt":"Out-of-distribution (OOD) graph generalization are critical for many real-world applications. Existing methods neglect to discard spurious or noisy features of inputs, which are irrelevant to the label. Besides, they mainly conduct instance-level class-invariant graph learning and fail to utilize the structural class relationships between graph instances. In this work, we endeavor to address these issues in a unified framework, dubbed Individual and Structural Graph Information Bottlenecks (IS-GIB). To remove class spurious feature caused by distribution shifts, we propose Individual Graph Inf"},"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":"2306.15902","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-06-28T03:52:41Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"cfc33aa7fa2fa940dcc17f527b12f53429b25a4f13444babfa9fc5485bbe4832","abstract_canon_sha256":"33bad5e6c8f783fbaa758cf05360d7d8a4111ae445be6dbe3205c557df51027c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:25:46.131868Z","signature_b64":"l7yyz4DfvUboXoF3+Y+lmjmMFfJjUov38ciNWIso81mdoSn2D2oZkWfZv2y3/35sygtaYJpCQOf70nniqvb9AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"54b0785f469576c95d4cfcf453da1a264c5f31abc078c1ce5bbfe29347087b9a","last_reissued_at":"2026-07-05T06:25:46.131369Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:25:46.131369Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Individual and Structural Graph Information Bottlenecks for Out-of-Distribution Generalization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Bin Cui, Heyuan Wang, Jiayi Zheng, Ling Yang, Shenda Hong, Wentao Zhang, Zhilin Huang, Zhongyi Liu","submitted_at":"2023-06-28T03:52:41Z","abstract_excerpt":"Out-of-distribution (OOD) graph generalization are critical for many real-world applications. Existing methods neglect to discard spurious or noisy features of inputs, which are irrelevant to the label. Besides, they mainly conduct instance-level class-invariant graph learning and fail to utilize the structural class relationships between graph instances. In this work, we endeavor to address these issues in a unified framework, dubbed Individual and Structural Graph Information Bottlenecks (IS-GIB). To remove class spurious feature caused by distribution shifts, we propose Individual Graph Inf"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.15902","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/2306.15902/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":"2306.15902","created_at":"2026-07-05T06:25:46.131434+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.15902v1","created_at":"2026-07-05T06:25:46.131434+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.15902","created_at":"2026-07-05T06:25:46.131434+00:00"},{"alias_kind":"pith_short_12","alias_value":"KSYHQX2GSV3M","created_at":"2026-07-05T06:25:46.131434+00:00"},{"alias_kind":"pith_short_16","alias_value":"KSYHQX2GSV3MSXKM","created_at":"2026-07-05T06:25:46.131434+00:00"},{"alias_kind":"pith_short_8","alias_value":"KSYHQX2G","created_at":"2026-07-05T06:25:46.131434+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.12226","citing_title":"Learning Causality for Modern Machine Learning","ref_index":68,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KSYHQX2GSV3MSXKM7T2FHWQ2EZ","json":"https://pith.science/pith/KSYHQX2GSV3MSXKM7T2FHWQ2EZ.json","graph_json":"https://pith.science/api/pith-number/KSYHQX2GSV3MSXKM7T2FHWQ2EZ/graph.json","events_json":"https://pith.science/api/pith-number/KSYHQX2GSV3MSXKM7T2FHWQ2EZ/events.json","paper":"https://pith.science/paper/KSYHQX2G"},"agent_actions":{"view_html":"https://pith.science/pith/KSYHQX2GSV3MSXKM7T2FHWQ2EZ","download_json":"https://pith.science/pith/KSYHQX2GSV3MSXKM7T2FHWQ2EZ.json","view_paper":"https://pith.science/paper/KSYHQX2G","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.15902&json=true","fetch_graph":"https://pith.science/api/pith-number/KSYHQX2GSV3MSXKM7T2FHWQ2EZ/graph.json","fetch_events":"https://pith.science/api/pith-number/KSYHQX2GSV3MSXKM7T2FHWQ2EZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KSYHQX2GSV3MSXKM7T2FHWQ2EZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KSYHQX2GSV3MSXKM7T2FHWQ2EZ/action/storage_attestation","attest_author":"https://pith.science/pith/KSYHQX2GSV3MSXKM7T2FHWQ2EZ/action/author_attestation","sign_citation":"https://pith.science/pith/KSYHQX2GSV3MSXKM7T2FHWQ2EZ/action/citation_signature","submit_replication":"https://pith.science/pith/KSYHQX2GSV3MSXKM7T2FHWQ2EZ/action/replication_record"}},"created_at":"2026-07-05T06:25:46.131434+00:00","updated_at":"2026-07-05T06:25:46.131434+00:00"}