{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:PWJY5OIJRA5YG32FRNZTI5BUQG","short_pith_number":"pith:PWJY5OIJ","canonical_record":{"source":{"id":"2306.09623","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-06-16T04:40:59Z","cross_cats_sorted":[],"title_canon_sha256":"24f95a1722685a7af080ecac4880501a7b3cb5b1b920a3f7c665b22da2063b84","abstract_canon_sha256":"0d465b20b6445cefec89d1791533148e39dffbaf4e2cc586426dec49a0705079"},"schema_version":"1.0"},"canonical_sha256":"7d938eb909883b836f458b7334743481981edbe099b79aa4e9f16e196e7e1873","source":{"kind":"arxiv","id":"2306.09623","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.09623","created_at":"2026-07-05T06:22:07Z"},{"alias_kind":"arxiv_version","alias_value":"2306.09623v2","created_at":"2026-07-05T06:22:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.09623","created_at":"2026-07-05T06:22:07Z"},{"alias_kind":"pith_short_12","alias_value":"PWJY5OIJRA5Y","created_at":"2026-07-05T06:22:07Z"},{"alias_kind":"pith_short_16","alias_value":"PWJY5OIJRA5YG32F","created_at":"2026-07-05T06:22:07Z"},{"alias_kind":"pith_short_8","alias_value":"PWJY5OIJ","created_at":"2026-07-05T06:22:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:PWJY5OIJRA5YG32FRNZTI5BUQG","target":"record","payload":{"canonical_record":{"source":{"id":"2306.09623","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-06-16T04:40:59Z","cross_cats_sorted":[],"title_canon_sha256":"24f95a1722685a7af080ecac4880501a7b3cb5b1b920a3f7c665b22da2063b84","abstract_canon_sha256":"0d465b20b6445cefec89d1791533148e39dffbaf4e2cc586426dec49a0705079"},"schema_version":"1.0"},"canonical_sha256":"7d938eb909883b836f458b7334743481981edbe099b79aa4e9f16e196e7e1873","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:22:07.695085Z","signature_b64":"h601sIwTiYGyhUX4jUvNR3i+wAkX/j2st8hqL3YY2WoAGQDkAcZgNP4RPo230c/py39XwCM8xK31zV8YD8lHCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7d938eb909883b836f458b7334743481981edbe099b79aa4e9f16e196e7e1873","last_reissued_at":"2026-07-05T06:22:07.694660Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:22:07.694660Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2306.09623","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:22:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9BFmWiF8wnRZz9O4Vf+s9+EK2R2rdsQQ2gEategBXKQYurwEUD60nGsg6aMukG7rtvSwZHm5RHaR7K13WULaDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T18:30:58.784621Z"},"content_sha256":"597f2cac603d9d342b88e76bdce115e8ce7d5441f0c92faa004fd4ab68e1e7d3","schema_version":"1.0","event_id":"sha256:597f2cac603d9d342b88e76bdce115e8ce7d5441f0c92faa004fd4ab68e1e7d3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:PWJY5OIJRA5YG32FRNZTI5BUQG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"From Hypergraph Energy Functions to Hypergraph Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"David Wipf, Quan Gan, Xipeng Qiu, Xuanjing Huang, Yuxin Wang","submitted_at":"2023-06-16T04:40:59Z","abstract_excerpt":"Hypergraphs are a powerful abstraction for representing higher-order interactions between entities of interest. To exploit these relationships in making downstream predictions, a variety of hypergraph neural network architectures have recently been proposed, in large part building upon precursors from the more traditional graph neural network (GNN) literature. Somewhat differently, in this paper we begin by presenting an expressive family of parameterized, hypergraph-regularized energy functions. We then demonstrate how minimizers of these energies effectively serve as node embeddings that, wh"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.09623","kind":"arxiv","version":2},"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/2306.09623/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:22:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"heeT0RT9Qr200n+LaxpD2VdG/1O1qCbxeXZ9u4rSrYS33rauorDhHfmr+0ptw4uMSoWmrbXL2YikT67pAobMDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T18:30:58.785207Z"},"content_sha256":"81aae111ff0a6b238352b2f20659f4b797b03b1c8e1e50b57c353c8359b43d95","schema_version":"1.0","event_id":"sha256:81aae111ff0a6b238352b2f20659f4b797b03b1c8e1e50b57c353c8359b43d95"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PWJY5OIJRA5YG32FRNZTI5BUQG/bundle.json","state_url":"https://pith.science/pith/PWJY5OIJRA5YG32FRNZTI5BUQG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PWJY5OIJRA5YG32FRNZTI5BUQG/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-18T18:30:58Z","links":{"resolver":"https://pith.science/pith/PWJY5OIJRA5YG32FRNZTI5BUQG","bundle":"https://pith.science/pith/PWJY5OIJRA5YG32FRNZTI5BUQG/bundle.json","state":"https://pith.science/pith/PWJY5OIJRA5YG32FRNZTI5BUQG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PWJY5OIJRA5YG32FRNZTI5BUQG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:PWJY5OIJRA5YG32FRNZTI5BUQG","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":"0d465b20b6445cefec89d1791533148e39dffbaf4e2cc586426dec49a0705079","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-06-16T04:40:59Z","title_canon_sha256":"24f95a1722685a7af080ecac4880501a7b3cb5b1b920a3f7c665b22da2063b84"},"schema_version":"1.0","source":{"id":"2306.09623","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.09623","created_at":"2026-07-05T06:22:07Z"},{"alias_kind":"arxiv_version","alias_value":"2306.09623v2","created_at":"2026-07-05T06:22:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.09623","created_at":"2026-07-05T06:22:07Z"},{"alias_kind":"pith_short_12","alias_value":"PWJY5OIJRA5Y","created_at":"2026-07-05T06:22:07Z"},{"alias_kind":"pith_short_16","alias_value":"PWJY5OIJRA5YG32F","created_at":"2026-07-05T06:22:07Z"},{"alias_kind":"pith_short_8","alias_value":"PWJY5OIJ","created_at":"2026-07-05T06:22:07Z"}],"graph_snapshots":[{"event_id":"sha256:81aae111ff0a6b238352b2f20659f4b797b03b1c8e1e50b57c353c8359b43d95","target":"graph","created_at":"2026-07-05T06:22:07Z","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/2306.09623/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Hypergraphs are a powerful abstraction for representing higher-order interactions between entities of interest. To exploit these relationships in making downstream predictions, a variety of hypergraph neural network architectures have recently been proposed, in large part building upon precursors from the more traditional graph neural network (GNN) literature. Somewhat differently, in this paper we begin by presenting an expressive family of parameterized, hypergraph-regularized energy functions. We then demonstrate how minimizers of these energies effectively serve as node embeddings that, wh","authors_text":"David Wipf, Quan Gan, Xipeng Qiu, Xuanjing Huang, Yuxin Wang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-06-16T04:40:59Z","title":"From Hypergraph Energy Functions to Hypergraph Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.09623","kind":"arxiv","version":2},"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":"sha256:597f2cac603d9d342b88e76bdce115e8ce7d5441f0c92faa004fd4ab68e1e7d3","target":"record","created_at":"2026-07-05T06:22:07Z","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":"0d465b20b6445cefec89d1791533148e39dffbaf4e2cc586426dec49a0705079","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-06-16T04:40:59Z","title_canon_sha256":"24f95a1722685a7af080ecac4880501a7b3cb5b1b920a3f7c665b22da2063b84"},"schema_version":"1.0","source":{"id":"2306.09623","kind":"arxiv","version":2}},"canonical_sha256":"7d938eb909883b836f458b7334743481981edbe099b79aa4e9f16e196e7e1873","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7d938eb909883b836f458b7334743481981edbe099b79aa4e9f16e196e7e1873","first_computed_at":"2026-07-05T06:22:07.694660Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:22:07.694660Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"h601sIwTiYGyhUX4jUvNR3i+wAkX/j2st8hqL3YY2WoAGQDkAcZgNP4RPo230c/py39XwCM8xK31zV8YD8lHCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T06:22:07.695085Z","signed_message":"canonical_sha256_bytes"},"source_id":"2306.09623","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:597f2cac603d9d342b88e76bdce115e8ce7d5441f0c92faa004fd4ab68e1e7d3","sha256:81aae111ff0a6b238352b2f20659f4b797b03b1c8e1e50b57c353c8359b43d95"],"state_sha256":"16d8a86a963b96eda13b200dc486dbf7044866177fb22c052ee42333827b7ad4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hbQ4XAbwcfTL4wTqKafiujMIUNRK/aaGnr8grAg0ovNtYTVjBOWGGxnbhaSm09MmzHH33UbGZs+fXO96QjYQBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T18:30:58.790682Z","bundle_sha256":"031d1250d982b91b4909bf3da2eaf253163e81313ae0d678e19cc0cbc50461cb"}}