{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:YW54GGMELAYOFG7C7QY2DCRY7I","short_pith_number":"pith:YW54GGME","schema_version":"1.0","canonical_sha256":"c5bbc319845830e29be2fc31a18a38fa17e212e9fecf75085916b85ab9270fff","source":{"kind":"arxiv","id":"1311.1033","version":2},"attestation_state":"computed","paper":{"title":"Nonparametric Bayesian models of hierarchical structure in complex networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Mikkel N. Schmidt, Morten M{\\o}rup, Tue Herlau","submitted_at":"2013-11-05T12:39:04Z","abstract_excerpt":"Analyzing and understanding the structure of complex relational data is important in many applications including analysis of the connectivity in the human brain. Such networks can have prominent patterns on different scales, calling for a hierarchically structured model. We propose two non-parametric Bayesian hierarchical network models based on Gibbs fragmentation tree priors, and demonstrate their ability to capture nested patterns in simulated networks. On real networks we demonstrate detection of hierarchical structure and show predictive performance on par with the state of the art. We en"},"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":"1311.1033","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2013-11-05T12:39:04Z","cross_cats_sorted":[],"title_canon_sha256":"7575f83b6259652022d33ae37a144d4626f8e8c0392670d0534a25f2ccbbd875","abstract_canon_sha256":"7c6bc82581e4726555cedc7030e79c42778bcc98378eded1807108900869818f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:06:34.013825Z","signature_b64":"HcF4CCxaCV4AXjuVCKpLdXvPAidoVvsVOZwHqIGBSddfQ2CVlPIH1as+ACnpcc282LkJo7vlbc0GZHWUmKKvBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c5bbc319845830e29be2fc31a18a38fa17e212e9fecf75085916b85ab9270fff","last_reissued_at":"2026-05-18T03:06:34.013330Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:06:34.013330Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Nonparametric Bayesian models of hierarchical structure in complex networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Mikkel N. Schmidt, Morten M{\\o}rup, Tue Herlau","submitted_at":"2013-11-05T12:39:04Z","abstract_excerpt":"Analyzing and understanding the structure of complex relational data is important in many applications including analysis of the connectivity in the human brain. Such networks can have prominent patterns on different scales, calling for a hierarchically structured model. We propose two non-parametric Bayesian hierarchical network models based on Gibbs fragmentation tree priors, and demonstrate their ability to capture nested patterns in simulated networks. On real networks we demonstrate detection of hierarchical structure and show predictive performance on par with the state of the art. We en"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1311.1033","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":"1311.1033","created_at":"2026-05-18T03:06:34.013399+00:00"},{"alias_kind":"arxiv_version","alias_value":"1311.1033v2","created_at":"2026-05-18T03:06:34.013399+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1311.1033","created_at":"2026-05-18T03:06:34.013399+00:00"},{"alias_kind":"pith_short_12","alias_value":"YW54GGMELAYO","created_at":"2026-05-18T12:28:09.283467+00:00"},{"alias_kind":"pith_short_16","alias_value":"YW54GGMELAYOFG7C","created_at":"2026-05-18T12:28:09.283467+00:00"},{"alias_kind":"pith_short_8","alias_value":"YW54GGME","created_at":"2026-05-18T12:28:09.283467+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/YW54GGMELAYOFG7C7QY2DCRY7I","json":"https://pith.science/pith/YW54GGMELAYOFG7C7QY2DCRY7I.json","graph_json":"https://pith.science/api/pith-number/YW54GGMELAYOFG7C7QY2DCRY7I/graph.json","events_json":"https://pith.science/api/pith-number/YW54GGMELAYOFG7C7QY2DCRY7I/events.json","paper":"https://pith.science/paper/YW54GGME"},"agent_actions":{"view_html":"https://pith.science/pith/YW54GGMELAYOFG7C7QY2DCRY7I","download_json":"https://pith.science/pith/YW54GGMELAYOFG7C7QY2DCRY7I.json","view_paper":"https://pith.science/paper/YW54GGME","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1311.1033&json=true","fetch_graph":"https://pith.science/api/pith-number/YW54GGMELAYOFG7C7QY2DCRY7I/graph.json","fetch_events":"https://pith.science/api/pith-number/YW54GGMELAYOFG7C7QY2DCRY7I/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YW54GGMELAYOFG7C7QY2DCRY7I/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YW54GGMELAYOFG7C7QY2DCRY7I/action/storage_attestation","attest_author":"https://pith.science/pith/YW54GGMELAYOFG7C7QY2DCRY7I/action/author_attestation","sign_citation":"https://pith.science/pith/YW54GGMELAYOFG7C7QY2DCRY7I/action/citation_signature","submit_replication":"https://pith.science/pith/YW54GGMELAYOFG7C7QY2DCRY7I/action/replication_record"}},"created_at":"2026-05-18T03:06:34.013399+00:00","updated_at":"2026-05-18T03:06:34.013399+00:00"}