{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:YPUEU77FOIXHD7ORM73YXY3OKL","short_pith_number":"pith:YPUEU77F","schema_version":"1.0","canonical_sha256":"c3e84a7fe5722e71fdd167f78be36e52ede3e532a39b6399159d2bda67ab7252","source":{"kind":"arxiv","id":"2507.20490","version":1},"attestation_state":"computed","paper":{"title":"HIAL: A New Paradigm for Hypergraph Active Learning via Influence Maximization","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Bing Zhou, Guoren Wang, Ronghua Li, Xunkai Li, Yanheng Hou, Zhenjun Li","submitted_at":"2025-07-28T02:58:21Z","abstract_excerpt":"In recent years, Hypergraph Neural Networks (HNNs) have demonstrated immense potential in handling complex systems with high-order interactions. However, acquiring large-scale, high-quality labeled data for these models is costly, making Active Learning (AL) a critical technique. Existing Graph Active Learning (GAL) methods, when applied to hypergraphs, often rely on techniques like \"clique expansion,\" which destroys the high-order structural information crucial to a hypergraph's success, thereby leading to suboptimal performance. To address this challenge, we introduce HIAL (Hypergraph Active"},"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":"2507.20490","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-28T02:58:21Z","cross_cats_sorted":[],"title_canon_sha256":"747c3b552bdbaab76958d9806d149e1ef9f9ff1e76c1f0956585351cc6b83a89","abstract_canon_sha256":"577b459aed150ee23363932226746bd66450bb2a20c970f89e0422737ef92a14"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:44:20.123893Z","signature_b64":"eL4EO2nIKasCTeiTs0Gx0biA63zvED5dNj0VBRUIxQZmfyfFb7icOtPQz6n3eJdcx4jUCGn5uP0I+w82JQIKBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c3e84a7fe5722e71fdd167f78be36e52ede3e532a39b6399159d2bda67ab7252","last_reissued_at":"2026-07-05T11:44:20.123418Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:44:20.123418Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"HIAL: A New Paradigm for Hypergraph Active Learning via Influence Maximization","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Bing Zhou, Guoren Wang, Ronghua Li, Xunkai Li, Yanheng Hou, Zhenjun Li","submitted_at":"2025-07-28T02:58:21Z","abstract_excerpt":"In recent years, Hypergraph Neural Networks (HNNs) have demonstrated immense potential in handling complex systems with high-order interactions. However, acquiring large-scale, high-quality labeled data for these models is costly, making Active Learning (AL) a critical technique. Existing Graph Active Learning (GAL) methods, when applied to hypergraphs, often rely on techniques like \"clique expansion,\" which destroys the high-order structural information crucial to a hypergraph's success, thereby leading to suboptimal performance. To address this challenge, we introduce HIAL (Hypergraph Active"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.20490","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/2507.20490/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":"2507.20490","created_at":"2026-07-05T11:44:20.123477+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.20490v1","created_at":"2026-07-05T11:44:20.123477+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.20490","created_at":"2026-07-05T11:44:20.123477+00:00"},{"alias_kind":"pith_short_12","alias_value":"YPUEU77FOIXH","created_at":"2026-07-05T11:44:20.123477+00:00"},{"alias_kind":"pith_short_16","alias_value":"YPUEU77FOIXHD7OR","created_at":"2026-07-05T11:44:20.123477+00:00"},{"alias_kind":"pith_short_8","alias_value":"YPUEU77F","created_at":"2026-07-05T11:44:20.123477+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/YPUEU77FOIXHD7ORM73YXY3OKL","json":"https://pith.science/pith/YPUEU77FOIXHD7ORM73YXY3OKL.json","graph_json":"https://pith.science/api/pith-number/YPUEU77FOIXHD7ORM73YXY3OKL/graph.json","events_json":"https://pith.science/api/pith-number/YPUEU77FOIXHD7ORM73YXY3OKL/events.json","paper":"https://pith.science/paper/YPUEU77F"},"agent_actions":{"view_html":"https://pith.science/pith/YPUEU77FOIXHD7ORM73YXY3OKL","download_json":"https://pith.science/pith/YPUEU77FOIXHD7ORM73YXY3OKL.json","view_paper":"https://pith.science/paper/YPUEU77F","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.20490&json=true","fetch_graph":"https://pith.science/api/pith-number/YPUEU77FOIXHD7ORM73YXY3OKL/graph.json","fetch_events":"https://pith.science/api/pith-number/YPUEU77FOIXHD7ORM73YXY3OKL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YPUEU77FOIXHD7ORM73YXY3OKL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YPUEU77FOIXHD7ORM73YXY3OKL/action/storage_attestation","attest_author":"https://pith.science/pith/YPUEU77FOIXHD7ORM73YXY3OKL/action/author_attestation","sign_citation":"https://pith.science/pith/YPUEU77FOIXHD7ORM73YXY3OKL/action/citation_signature","submit_replication":"https://pith.science/pith/YPUEU77FOIXHD7ORM73YXY3OKL/action/replication_record"}},"created_at":"2026-07-05T11:44:20.123477+00:00","updated_at":"2026-07-05T11:44:20.123477+00:00"}