{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:BWMFPEAPBGY75BU32MQSXGFZZG","short_pith_number":"pith:BWMFPEAP","canonical_record":{"source":{"id":"2203.16939","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-03-31T10:46:47Z","cross_cats_sorted":[],"title_canon_sha256":"2b74bd4b0e852e3f28a45c121e78a4ef3ab61d355eec51de3e992fe6b766f05c","abstract_canon_sha256":"51daa31347c42dbd379bdb584b148ff9e3597678840ae118de4a4db50a95ca52"},"schema_version":"1.0"},"canonical_sha256":"0d9857900f09b1fe869bd3212b98b9c996443f22b8ebccc142bc4bdb921e89a1","source":{"kind":"arxiv","id":"2203.16939","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.16939","created_at":"2026-07-05T04:43:00Z"},{"alias_kind":"arxiv_version","alias_value":"2203.16939v3","created_at":"2026-07-05T04:43:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.16939","created_at":"2026-07-05T04:43:00Z"},{"alias_kind":"pith_short_12","alias_value":"BWMFPEAPBGY7","created_at":"2026-07-05T04:43:00Z"},{"alias_kind":"pith_short_16","alias_value":"BWMFPEAPBGY75BU3","created_at":"2026-07-05T04:43:00Z"},{"alias_kind":"pith_short_8","alias_value":"BWMFPEAP","created_at":"2026-07-05T04:43:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:BWMFPEAPBGY75BU32MQSXGFZZG","target":"record","payload":{"canonical_record":{"source":{"id":"2203.16939","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-03-31T10:46:47Z","cross_cats_sorted":[],"title_canon_sha256":"2b74bd4b0e852e3f28a45c121e78a4ef3ab61d355eec51de3e992fe6b766f05c","abstract_canon_sha256":"51daa31347c42dbd379bdb584b148ff9e3597678840ae118de4a4db50a95ca52"},"schema_version":"1.0"},"canonical_sha256":"0d9857900f09b1fe869bd3212b98b9c996443f22b8ebccc142bc4bdb921e89a1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:43:00.651382Z","signature_b64":"vj6mh19a6F7ncfW1g91jggI97MJZ9tGMv/X//zvnVSz2FA1fKwqwtQig3W4kTGA+tAT0MCsQMerQLZoOSgYCAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0d9857900f09b1fe869bd3212b98b9c996443f22b8ebccc142bc4bdb921e89a1","last_reissued_at":"2026-07-05T04:43:00.650957Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:43:00.650957Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2203.16939","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:43:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iGxHemoUJk3nM/9HmV0YPhL78am7KdokBC9FDGAqcz3Dn/p6TWSeqTTrmv68iwnbkXXFgXAMlSQsdsU5fn8gAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T04:53:41.195826Z"},"content_sha256":"1d1d1040a3ad5786d08f1acf8e552da254ec6adf20b20c8d8a62c8bcfa7dc668","schema_version":"1.0","event_id":"sha256:1d1d1040a3ad5786d08f1acf8e552da254ec6adf20b20c8d8a62c8bcfa7dc668"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:BWMFPEAPBGY75BU32MQSXGFZZG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Hypergraph Convolutional Networks via Equivalency between Hypergraphs and Undirected Graphs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Fuyang Li, Jiying Zhang, Junzhou Huang, Tingyang Xu, Xi Xiao, Yatao Bian, Yu Rong","submitted_at":"2022-03-31T10:46:47Z","abstract_excerpt":"As a powerful tool for modeling complex relationships, hypergraphs are gaining popularity from the graph learning community. However, commonly used frameworks in deep hypergraph learning focus on hypergraphs with edge-independent vertex weights (EIVWs), without considering hypergraphs with edge-dependent vertex weights (EDVWs) that have more modeling power. To compensate for this, we present General Hypergraph Spectral Convolution (GHSC), a general learning framework that not only handles EDVW and EIVW hypergraphs, but more importantly, enables theoretically explicitly utilizing the existing p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.16939","kind":"arxiv","version":3},"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/2203.16939/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":"2026-07-05T04:43:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ql52fEsxFpk9eWBiP7ljMxv9HQK9/FdO5N3bJ1aguWeoVaEBTweWQrAUk/u5VnHgtVzUekc9dRUn4GScMikOBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T04:53:41.196436Z"},"content_sha256":"eb6694b481c32f81e98f83604e736529ff28976580f73425cfb418165d10193d","schema_version":"1.0","event_id":"sha256:eb6694b481c32f81e98f83604e736529ff28976580f73425cfb418165d10193d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BWMFPEAPBGY75BU32MQSXGFZZG/bundle.json","state_url":"https://pith.science/pith/BWMFPEAPBGY75BU32MQSXGFZZG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BWMFPEAPBGY75BU32MQSXGFZZG/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":"2026-07-09T04:53:41Z","links":{"resolver":"https://pith.science/pith/BWMFPEAPBGY75BU32MQSXGFZZG","bundle":"https://pith.science/pith/BWMFPEAPBGY75BU32MQSXGFZZG/bundle.json","state":"https://pith.science/pith/BWMFPEAPBGY75BU32MQSXGFZZG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BWMFPEAPBGY75BU32MQSXGFZZG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:BWMFPEAPBGY75BU32MQSXGFZZG","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":"51daa31347c42dbd379bdb584b148ff9e3597678840ae118de4a4db50a95ca52","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-03-31T10:46:47Z","title_canon_sha256":"2b74bd4b0e852e3f28a45c121e78a4ef3ab61d355eec51de3e992fe6b766f05c"},"schema_version":"1.0","source":{"id":"2203.16939","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.16939","created_at":"2026-07-05T04:43:00Z"},{"alias_kind":"arxiv_version","alias_value":"2203.16939v3","created_at":"2026-07-05T04:43:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.16939","created_at":"2026-07-05T04:43:00Z"},{"alias_kind":"pith_short_12","alias_value":"BWMFPEAPBGY7","created_at":"2026-07-05T04:43:00Z"},{"alias_kind":"pith_short_16","alias_value":"BWMFPEAPBGY75BU3","created_at":"2026-07-05T04:43:00Z"},{"alias_kind":"pith_short_8","alias_value":"BWMFPEAP","created_at":"2026-07-05T04:43:00Z"}],"graph_snapshots":[{"event_id":"sha256:eb6694b481c32f81e98f83604e736529ff28976580f73425cfb418165d10193d","target":"graph","created_at":"2026-07-05T04:43:00Z","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/2203.16939/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"As a powerful tool for modeling complex relationships, hypergraphs are gaining popularity from the graph learning community. However, commonly used frameworks in deep hypergraph learning focus on hypergraphs with edge-independent vertex weights (EIVWs), without considering hypergraphs with edge-dependent vertex weights (EDVWs) that have more modeling power. To compensate for this, we present General Hypergraph Spectral Convolution (GHSC), a general learning framework that not only handles EDVW and EIVW hypergraphs, but more importantly, enables theoretically explicitly utilizing the existing p","authors_text":"Fuyang Li, Jiying Zhang, Junzhou Huang, Tingyang Xu, Xi Xiao, Yatao Bian, Yu Rong","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-03-31T10:46:47Z","title":"Hypergraph Convolutional Networks via Equivalency between Hypergraphs and Undirected Graphs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.16939","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":"sha256:1d1d1040a3ad5786d08f1acf8e552da254ec6adf20b20c8d8a62c8bcfa7dc668","target":"record","created_at":"2026-07-05T04:43:00Z","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":"51daa31347c42dbd379bdb584b148ff9e3597678840ae118de4a4db50a95ca52","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-03-31T10:46:47Z","title_canon_sha256":"2b74bd4b0e852e3f28a45c121e78a4ef3ab61d355eec51de3e992fe6b766f05c"},"schema_version":"1.0","source":{"id":"2203.16939","kind":"arxiv","version":3}},"canonical_sha256":"0d9857900f09b1fe869bd3212b98b9c996443f22b8ebccc142bc4bdb921e89a1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0d9857900f09b1fe869bd3212b98b9c996443f22b8ebccc142bc4bdb921e89a1","first_computed_at":"2026-07-05T04:43:00.650957Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:43:00.650957Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"vj6mh19a6F7ncfW1g91jggI97MJZ9tGMv/X//zvnVSz2FA1fKwqwtQig3W4kTGA+tAT0MCsQMerQLZoOSgYCAA==","signature_status":"signed_v1","signed_at":"2026-07-05T04:43:00.651382Z","signed_message":"canonical_sha256_bytes"},"source_id":"2203.16939","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1d1d1040a3ad5786d08f1acf8e552da254ec6adf20b20c8d8a62c8bcfa7dc668","sha256:eb6694b481c32f81e98f83604e736529ff28976580f73425cfb418165d10193d"],"state_sha256":"aa6e82bea91e30ab5d4cddbb8b4e2b5766d5bf209e88fe86fdc8f831c17fddc2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NqA/k19BqVibjNC/iRBX47GABoPhZMpRhnB+Fa970iNQNfmYz2LlODdaGe4tYKEbtyUe9W/qU2HLfXgCUJIOCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-09T04:53:41.199152Z","bundle_sha256":"8605ef7d55300b12872f74144085a1399aa81cac5c326cb67ed05a8223b3f081"}}