{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:IEZROGVNZOICHR7C4TPFDF7PGK","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":"4a30795a9aa50fd7e77686fcf02b78873d28d6f10cc14208a96837f6d19d983f","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-01-19T18:37:50Z","title_canon_sha256":"ea872521e0a987f3ccc64ec6a7fa761d6fd0bb77e701ce3a38d61e7e72765043"},"schema_version":"1.0","source":{"id":"2101.07773","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2101.07773","created_at":"2026-07-05T02:08:01Z"},{"alias_kind":"arxiv_version","alias_value":"2101.07773v1","created_at":"2026-07-05T02:08:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2101.07773","created_at":"2026-07-05T02:08:01Z"},{"alias_kind":"pith_short_12","alias_value":"IEZROGVNZOIC","created_at":"2026-07-05T02:08:01Z"},{"alias_kind":"pith_short_16","alias_value":"IEZROGVNZOICHR7C","created_at":"2026-07-05T02:08:01Z"},{"alias_kind":"pith_short_8","alias_value":"IEZROGVN","created_at":"2026-07-05T02:08:01Z"}],"graph_snapshots":[{"event_id":"sha256:8f2d3d6bf29a091b20cf419b673ba4f99b81eb7ed79dc5b2c7a69f37220721d4","target":"graph","created_at":"2026-07-05T02:08:01Z","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/2101.07773/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graph representation learning has made major strides over the past decade. However, in many relational domains, the input data are not suited for simple graph representations as the relationships between entities go beyond pairwise interactions. In such cases, the relationships in the data are better represented as hyperedges (set of entities) of a non-uniform hypergraph. While there have been works on principled methods for learning representations of nodes of a hypergraph, these approaches are limited in their applicability to tasks on non-uniform hypergraphs (hyperedges with different cardi","authors_text":"Balasubramaniam Srinivasan, Da Zheng, George Karypis","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-01-19T18:37:50Z","title":"Learning over Families of Sets -- Hypergraph Representation Learning for Higher Order Tasks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2101.07773","kind":"arxiv","version":1},"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:c6e2b1bddc1f5236ec203f10b4da3105af7959ae457f065f8687b5ec812ee170","target":"record","created_at":"2026-07-05T02:08:01Z","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":"4a30795a9aa50fd7e77686fcf02b78873d28d6f10cc14208a96837f6d19d983f","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-01-19T18:37:50Z","title_canon_sha256":"ea872521e0a987f3ccc64ec6a7fa761d6fd0bb77e701ce3a38d61e7e72765043"},"schema_version":"1.0","source":{"id":"2101.07773","kind":"arxiv","version":1}},"canonical_sha256":"4133171aadcb9023c7e2e4de5197ef32b01fc16155ce32611201b2859728d6b6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4133171aadcb9023c7e2e4de5197ef32b01fc16155ce32611201b2859728d6b6","first_computed_at":"2026-07-05T02:08:01.134779Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:08:01.134779Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"A+qXFf9fu/B9aGBW2aPrZkvbwt4lI9SbYJG3EL2yT4l4IZxh5dDCBaDECZUeznJpcfwGXV8uSVyB1PsHaEyYCg==","signature_status":"signed_v1","signed_at":"2026-07-05T02:08:01.135115Z","signed_message":"canonical_sha256_bytes"},"source_id":"2101.07773","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c6e2b1bddc1f5236ec203f10b4da3105af7959ae457f065f8687b5ec812ee170","sha256:8f2d3d6bf29a091b20cf419b673ba4f99b81eb7ed79dc5b2c7a69f37220721d4"],"state_sha256":"dd49a664ed84359ec382e7d5ee09257fc9f94beea3177351dc0fe1badf611152"}