{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:RWFHKD3CF35Y2TYQNLE2U7SHXM","short_pith_number":"pith:RWFHKD3C","schema_version":"1.0","canonical_sha256":"8d8a750f622efb8d4f106ac9aa7e47bb18a2945baf5a173ccb720acdf8b34f11","source":{"kind":"arxiv","id":"2504.17276","version":1},"attestation_state":"computed","paper":{"title":"HeRB: Heterophily-Resolved Structure Balancer for Graph Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Ke-Jia Chen, Wenhui Mu, Zheng Liu","submitted_at":"2025-04-24T06:04:59Z","abstract_excerpt":"Recent research has witnessed the remarkable progress of Graph Neural Networks (GNNs) in the realm of graph data representation. However, GNNs still encounter the challenge of structural imbalance. Prior solutions to this problem did not take graph heterophily into account, namely that connected nodes process distinct labels or features, thus resulting in a deficiency in effectiveness. Upon verifying the impact of heterophily on solving the structural imbalance problem, we propose to rectify the heterophily first and then transfer homophilic knowledge. To the end, we devise a method named HeRB"},"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":"2504.17276","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-04-24T06:04:59Z","cross_cats_sorted":[],"title_canon_sha256":"87a76046794e03980a89b4169be4ed57546f21b7bd130cda3c6faad16e740cfe","abstract_canon_sha256":"9775febca0e19f8f49afc83f6f0f64e3cdd6181f40ef280926f2ce6b8f3de2f7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:53:25.699665Z","signature_b64":"sW8uFvZ52SKE+XNYObgYKgm1m5dchEViKf1QeFbQ4yK6OhLP+lBXtYLshVuS9+KHtgiMbyZDAMJIFuQ9KiOpAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8d8a750f622efb8d4f106ac9aa7e47bb18a2945baf5a173ccb720acdf8b34f11","last_reissued_at":"2026-07-05T10:53:25.699233Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:53:25.699233Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"HeRB: Heterophily-Resolved Structure Balancer for Graph Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Ke-Jia Chen, Wenhui Mu, Zheng Liu","submitted_at":"2025-04-24T06:04:59Z","abstract_excerpt":"Recent research has witnessed the remarkable progress of Graph Neural Networks (GNNs) in the realm of graph data representation. However, GNNs still encounter the challenge of structural imbalance. Prior solutions to this problem did not take graph heterophily into account, namely that connected nodes process distinct labels or features, thus resulting in a deficiency in effectiveness. Upon verifying the impact of heterophily on solving the structural imbalance problem, we propose to rectify the heterophily first and then transfer homophilic knowledge. To the end, we devise a method named HeRB"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.17276","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/2504.17276/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":"2504.17276","created_at":"2026-07-05T10:53:25.699295+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.17276v1","created_at":"2026-07-05T10:53:25.699295+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.17276","created_at":"2026-07-05T10:53:25.699295+00:00"},{"alias_kind":"pith_short_12","alias_value":"RWFHKD3CF35Y","created_at":"2026-07-05T10:53:25.699295+00:00"},{"alias_kind":"pith_short_16","alias_value":"RWFHKD3CF35Y2TYQ","created_at":"2026-07-05T10:53:25.699295+00:00"},{"alias_kind":"pith_short_8","alias_value":"RWFHKD3C","created_at":"2026-07-05T10:53:25.699295+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/RWFHKD3CF35Y2TYQNLE2U7SHXM","json":"https://pith.science/pith/RWFHKD3CF35Y2TYQNLE2U7SHXM.json","graph_json":"https://pith.science/api/pith-number/RWFHKD3CF35Y2TYQNLE2U7SHXM/graph.json","events_json":"https://pith.science/api/pith-number/RWFHKD3CF35Y2TYQNLE2U7SHXM/events.json","paper":"https://pith.science/paper/RWFHKD3C"},"agent_actions":{"view_html":"https://pith.science/pith/RWFHKD3CF35Y2TYQNLE2U7SHXM","download_json":"https://pith.science/pith/RWFHKD3CF35Y2TYQNLE2U7SHXM.json","view_paper":"https://pith.science/paper/RWFHKD3C","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.17276&json=true","fetch_graph":"https://pith.science/api/pith-number/RWFHKD3CF35Y2TYQNLE2U7SHXM/graph.json","fetch_events":"https://pith.science/api/pith-number/RWFHKD3CF35Y2TYQNLE2U7SHXM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RWFHKD3CF35Y2TYQNLE2U7SHXM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RWFHKD3CF35Y2TYQNLE2U7SHXM/action/storage_attestation","attest_author":"https://pith.science/pith/RWFHKD3CF35Y2TYQNLE2U7SHXM/action/author_attestation","sign_citation":"https://pith.science/pith/RWFHKD3CF35Y2TYQNLE2U7SHXM/action/citation_signature","submit_replication":"https://pith.science/pith/RWFHKD3CF35Y2TYQNLE2U7SHXM/action/replication_record"}},"created_at":"2026-07-05T10:53:25.699295+00:00","updated_at":"2026-07-05T10:53:25.699295+00:00"}