{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:GVH3TGY5OJ2OFN2DHWZSEZMJ73","short_pith_number":"pith:GVH3TGY5","schema_version":"1.0","canonical_sha256":"354fb99b1d7274e2b7433db3226589fec8b8d1f17418576d3291b5d437eb17ca","source":{"kind":"arxiv","id":"1503.04567","version":2},"attestation_state":"computed","paper":{"title":"Learning Mixed Membership Community Models in Social Tagging Networks through Tensor Methods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Anima Anandkumar, Hanie Sedghi","submitted_at":"2015-03-16T08:27:54Z","abstract_excerpt":"Community detection in graphs has been extensively studied both in theory and in applications. However, detecting communities in hypergraphs is more challenging. In this paper, we propose a tensor decomposition approach for guaranteed learning of communities in a special class of hypergraphs modeling social tagging systems or folksonomies. A folksonomy is a tripartite 3-uniform hypergraph consisting of (user, tag, resource) hyperedges. We posit a probabilistic mixed membership community model, and prove that the tensor method consistently learns the communities under efficient sample complexit"},"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":"1503.04567","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-03-16T08:27:54Z","cross_cats_sorted":["cs.SI","stat.ML"],"title_canon_sha256":"f96f8b1122956abed83c0b7f46d941b46b369a53c51f539a03e35382b9ffd676","abstract_canon_sha256":"f51e8a3a20303b67dd09292d86231357749bae01e44e754c76e36ef7cd4b504f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:18:03.421874Z","signature_b64":"XcT6oAPshCtqM0g50W5n+uPKkAvAsKG7y/+1ZUAqwp2W5iTXwEJuRvZFjks92ZQoi27kU/LFivaol6pPWkYbDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"354fb99b1d7274e2b7433db3226589fec8b8d1f17418576d3291b5d437eb17ca","last_reissued_at":"2026-05-18T02:18:03.421161Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:18:03.421161Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Mixed Membership Community Models in Social Tagging Networks through Tensor Methods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Anima Anandkumar, Hanie Sedghi","submitted_at":"2015-03-16T08:27:54Z","abstract_excerpt":"Community detection in graphs has been extensively studied both in theory and in applications. However, detecting communities in hypergraphs is more challenging. In this paper, we propose a tensor decomposition approach for guaranteed learning of communities in a special class of hypergraphs modeling social tagging systems or folksonomies. A folksonomy is a tripartite 3-uniform hypergraph consisting of (user, tag, resource) hyperedges. We posit a probabilistic mixed membership community model, and prove that the tensor method consistently learns the communities under efficient sample complexit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1503.04567","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":""},"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":"1503.04567","created_at":"2026-05-18T02:18:03.421284+00:00"},{"alias_kind":"arxiv_version","alias_value":"1503.04567v2","created_at":"2026-05-18T02:18:03.421284+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1503.04567","created_at":"2026-05-18T02:18:03.421284+00:00"},{"alias_kind":"pith_short_12","alias_value":"GVH3TGY5OJ2O","created_at":"2026-05-18T12:29:22.688609+00:00"},{"alias_kind":"pith_short_16","alias_value":"GVH3TGY5OJ2OFN2D","created_at":"2026-05-18T12:29:22.688609+00:00"},{"alias_kind":"pith_short_8","alias_value":"GVH3TGY5","created_at":"2026-05-18T12:29:22.688609+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/GVH3TGY5OJ2OFN2DHWZSEZMJ73","json":"https://pith.science/pith/GVH3TGY5OJ2OFN2DHWZSEZMJ73.json","graph_json":"https://pith.science/api/pith-number/GVH3TGY5OJ2OFN2DHWZSEZMJ73/graph.json","events_json":"https://pith.science/api/pith-number/GVH3TGY5OJ2OFN2DHWZSEZMJ73/events.json","paper":"https://pith.science/paper/GVH3TGY5"},"agent_actions":{"view_html":"https://pith.science/pith/GVH3TGY5OJ2OFN2DHWZSEZMJ73","download_json":"https://pith.science/pith/GVH3TGY5OJ2OFN2DHWZSEZMJ73.json","view_paper":"https://pith.science/paper/GVH3TGY5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1503.04567&json=true","fetch_graph":"https://pith.science/api/pith-number/GVH3TGY5OJ2OFN2DHWZSEZMJ73/graph.json","fetch_events":"https://pith.science/api/pith-number/GVH3TGY5OJ2OFN2DHWZSEZMJ73/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GVH3TGY5OJ2OFN2DHWZSEZMJ73/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GVH3TGY5OJ2OFN2DHWZSEZMJ73/action/storage_attestation","attest_author":"https://pith.science/pith/GVH3TGY5OJ2OFN2DHWZSEZMJ73/action/author_attestation","sign_citation":"https://pith.science/pith/GVH3TGY5OJ2OFN2DHWZSEZMJ73/action/citation_signature","submit_replication":"https://pith.science/pith/GVH3TGY5OJ2OFN2DHWZSEZMJ73/action/replication_record"}},"created_at":"2026-05-18T02:18:03.421284+00:00","updated_at":"2026-05-18T02:18:03.421284+00:00"}