{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:EV3CA5Z3GK3CTF6FIBWEQVHXLR","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"2916067939f0ba05cba8911aef36f1ddd74a396c461efc8e43276d71855fd5fb","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-07-07T06:26:44Z","title_canon_sha256":"9fa0b712479a0cd751204882281cb41ff300679c8b4db4e0edb4703a3d018775"},"schema_version":"1.0","source":{"id":"2307.03411","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.03411","created_at":"2026-07-05T09:54:42Z"},{"alias_kind":"arxiv_version","alias_value":"2307.03411v3","created_at":"2026-07-05T09:54:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.03411","created_at":"2026-07-05T09:54:42Z"},{"alias_kind":"pith_short_12","alias_value":"EV3CA5Z3GK3C","created_at":"2026-07-05T09:54:42Z"},{"alias_kind":"pith_short_16","alias_value":"EV3CA5Z3GK3CTF6F","created_at":"2026-07-05T09:54:42Z"},{"alias_kind":"pith_short_8","alias_value":"EV3CA5Z3","created_at":"2026-07-05T09:54:42Z"}],"graph_snapshots":[{"event_id":"sha256:e280e763f672326d478a4202ba91e604be791a4e213c4ea1fd781215b07128b9","target":"graph","created_at":"2026-07-05T09:54:42Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2307.03411/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graph neural network (GNN) has gained increasing popularity in recent years owing to its capability and flexibility in modeling complex graph structure data. Among all graph learning methods, hypergraph learning is a technique for exploring the implicit higher-order correlations when training the embedding space of the graph. In this paper, we propose a hypergraph learning framework named LFH that is capable of dynamic hyperedge construction and attentive embedding update utilizing the heterogeneity attributes of the graph. Specifically, in our framework, the high-quality features are first ge","authors_text":"Jiong Jin, Peng Qi, Tiehua Zhang, Xingjun Ma, Yuze Liu, Zhijun Ding, Zhishu Shen","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-07-07T06:26:44Z","title":"Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.03411","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:bb95ab80ca17f9fa9562cb37d6a6360c011bc58b1ace4a5e58004fd42bf08007","target":"record","created_at":"2026-07-05T09:54:42Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"2916067939f0ba05cba8911aef36f1ddd74a396c461efc8e43276d71855fd5fb","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-07-07T06:26:44Z","title_canon_sha256":"9fa0b712479a0cd751204882281cb41ff300679c8b4db4e0edb4703a3d018775"},"schema_version":"1.0","source":{"id":"2307.03411","kind":"arxiv","version":3}},"canonical_sha256":"257620773b32b62997c5406c4854f75c670a300b432d8ab393e188099589f827","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"257620773b32b62997c5406c4854f75c670a300b432d8ab393e188099589f827","first_computed_at":"2026-07-05T09:54:42.823870Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:54:42.823870Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uYMmCaXN9Pc2vMHBbVuY0AHR2fLD8VgkWkw8U4Q+B25prf1R3zuv8cuIRoKih4yXDLQROgFjXl/VWkP9eXWgBA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:54:42.824274Z","signed_message":"canonical_sha256_bytes"},"source_id":"2307.03411","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bb95ab80ca17f9fa9562cb37d6a6360c011bc58b1ace4a5e58004fd42bf08007","sha256:e280e763f672326d478a4202ba91e604be791a4e213c4ea1fd781215b07128b9"],"state_sha256":"4236e26448e3617da060db36cb0bc73c31531ce844bad52a7363edb02f6285d6"}