{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:CBQRVK7JG6SMC3ICFPSDK34W4R","short_pith_number":"pith:CBQRVK7J","schema_version":"1.0","canonical_sha256":"10611aabe937a4c16d022be4356f96e456395df2124bcaf6afc5dfdc3269038a","source":{"kind":"arxiv","id":"2505.04340","version":1},"attestation_state":"computed","paper":{"title":"Multi-Granular Attention based Heterogeneous Hypergraph Neural Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Hong Jin, Jie Yin, Kaicheng Zhou, Lan You, Zhifeng Zhou","submitted_at":"2025-05-07T11:42:00Z","abstract_excerpt":"Heterogeneous graph neural networks (HeteGNNs) have demonstrated strong abilities to learn node representations by effectively extracting complex structural and semantic information in heterogeneous graphs. Most of the prevailing HeteGNNs follow the neighborhood aggregation paradigm, leveraging meta-path based message passing to learn latent node representations. However, due to the pairwise nature of meta-paths, these models fail to capture high-order relations among nodes, resulting in suboptimal performance. Additionally, the challenge of ``over-squashing'', where long-range message passing"},"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":"2505.04340","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-07T11:42:00Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"7a316b6e3d4ad610b31d90faaa671c2ef23e786289b96ea299e0fd68f4cbe762","abstract_canon_sha256":"4daaa6808efe63b5f486874f75d4249834ffbee863d883d6d0f7cacd6f198a4d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:59:40.289460Z","signature_b64":"bmdlmwDhtNv4WmXYFX5GNkjN7jdChWdQVbAYynyESHlyalMWh9paa+4Nj3f2Mm5HqMAHK/zMD13MxArLmrdKAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"10611aabe937a4c16d022be4356f96e456395df2124bcaf6afc5dfdc3269038a","last_reissued_at":"2026-07-05T10:59:40.288936Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:59:40.288936Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-Granular Attention based Heterogeneous Hypergraph Neural Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Hong Jin, Jie Yin, Kaicheng Zhou, Lan You, Zhifeng Zhou","submitted_at":"2025-05-07T11:42:00Z","abstract_excerpt":"Heterogeneous graph neural networks (HeteGNNs) have demonstrated strong abilities to learn node representations by effectively extracting complex structural and semantic information in heterogeneous graphs. Most of the prevailing HeteGNNs follow the neighborhood aggregation paradigm, leveraging meta-path based message passing to learn latent node representations. However, due to the pairwise nature of meta-paths, these models fail to capture high-order relations among nodes, resulting in suboptimal performance. Additionally, the challenge of ``over-squashing'', where long-range message passing"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.04340","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/2505.04340/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":"2505.04340","created_at":"2026-07-05T10:59:40.289000+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.04340v1","created_at":"2026-07-05T10:59:40.289000+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.04340","created_at":"2026-07-05T10:59:40.289000+00:00"},{"alias_kind":"pith_short_12","alias_value":"CBQRVK7JG6SM","created_at":"2026-07-05T10:59:40.289000+00:00"},{"alias_kind":"pith_short_16","alias_value":"CBQRVK7JG6SMC3IC","created_at":"2026-07-05T10:59:40.289000+00:00"},{"alias_kind":"pith_short_8","alias_value":"CBQRVK7J","created_at":"2026-07-05T10:59:40.289000+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/CBQRVK7JG6SMC3ICFPSDK34W4R","json":"https://pith.science/pith/CBQRVK7JG6SMC3ICFPSDK34W4R.json","graph_json":"https://pith.science/api/pith-number/CBQRVK7JG6SMC3ICFPSDK34W4R/graph.json","events_json":"https://pith.science/api/pith-number/CBQRVK7JG6SMC3ICFPSDK34W4R/events.json","paper":"https://pith.science/paper/CBQRVK7J"},"agent_actions":{"view_html":"https://pith.science/pith/CBQRVK7JG6SMC3ICFPSDK34W4R","download_json":"https://pith.science/pith/CBQRVK7JG6SMC3ICFPSDK34W4R.json","view_paper":"https://pith.science/paper/CBQRVK7J","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.04340&json=true","fetch_graph":"https://pith.science/api/pith-number/CBQRVK7JG6SMC3ICFPSDK34W4R/graph.json","fetch_events":"https://pith.science/api/pith-number/CBQRVK7JG6SMC3ICFPSDK34W4R/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CBQRVK7JG6SMC3ICFPSDK34W4R/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CBQRVK7JG6SMC3ICFPSDK34W4R/action/storage_attestation","attest_author":"https://pith.science/pith/CBQRVK7JG6SMC3ICFPSDK34W4R/action/author_attestation","sign_citation":"https://pith.science/pith/CBQRVK7JG6SMC3ICFPSDK34W4R/action/citation_signature","submit_replication":"https://pith.science/pith/CBQRVK7JG6SMC3ICFPSDK34W4R/action/replication_record"}},"created_at":"2026-07-05T10:59:40.289000+00:00","updated_at":"2026-07-05T10:59:40.289000+00:00"}