{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:SCRZR534HSL5QOL3A5O5QZHZV6","short_pith_number":"pith:SCRZR534","schema_version":"1.0","canonical_sha256":"90a398f77c3c97d8397b075dd864f9afae28185a12765d5fff481acf07b9a5d7","source":{"kind":"arxiv","id":"1808.06079","version":2},"attestation_state":"computed","paper":{"title":"Community detection in networks without observing edges","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","physics.soc-ph"],"primary_cat":"cs.SI","authors_text":"Leto Peel, Nick S. Jones, Renaud Lambiotte, Till Hoffmann","submitted_at":"2018-08-18T12:34:17Z","abstract_excerpt":"We develop a Bayesian hierarchical model to identify communities in networks for which we do not observe the edges directly, but instead observe a series of interdependent signals for each of the nodes. Fitting the model provides an end-to-end community detection algorithm that does not extract information as a sequence of point estimates but propagates uncertainties from the raw data to the community labels. Our approach naturally supports multiscale community detection as well as the selection of an optimal scale using model comparison. We study the properties of the algorithm using syntheti"},"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":"1808.06079","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2018-08-18T12:34:17Z","cross_cats_sorted":["cs.LG","physics.soc-ph"],"title_canon_sha256":"166c7e8e1f12eed85ef14db3204a83298566bd2383c2fc0faea26e319b00e761","abstract_canon_sha256":"9d634aeb230f12067172427f32d450b1d28de53db84b8973beff2147ff26e964"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:39:30.158273Z","signature_b64":"rumCwgkpcu2B2oKfzUdzOJ9D15Vr+j3G7bNFNUHylm4vOtUhhl7HORiz9BG2FYXF70YkjL1b/6uRGkksMBeqCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"90a398f77c3c97d8397b075dd864f9afae28185a12765d5fff481acf07b9a5d7","last_reissued_at":"2026-07-05T00:39:30.157611Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:39:30.157611Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Community detection in networks without observing edges","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","physics.soc-ph"],"primary_cat":"cs.SI","authors_text":"Leto Peel, Nick S. Jones, Renaud Lambiotte, Till Hoffmann","submitted_at":"2018-08-18T12:34:17Z","abstract_excerpt":"We develop a Bayesian hierarchical model to identify communities in networks for which we do not observe the edges directly, but instead observe a series of interdependent signals for each of the nodes. Fitting the model provides an end-to-end community detection algorithm that does not extract information as a sequence of point estimates but propagates uncertainties from the raw data to the community labels. Our approach naturally supports multiscale community detection as well as the selection of an optimal scale using model comparison. We study the properties of the algorithm using syntheti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.06079","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/1808.06079/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1808.06079","created_at":"2026-07-05T00:39:30.157676+00:00"},{"alias_kind":"arxiv_version","alias_value":"1808.06079v2","created_at":"2026-07-05T00:39:30.157676+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.06079","created_at":"2026-07-05T00:39:30.157676+00:00"},{"alias_kind":"pith_short_12","alias_value":"SCRZR534HSL5","created_at":"2026-07-05T00:39:30.157676+00:00"},{"alias_kind":"pith_short_16","alias_value":"SCRZR534HSL5QOL3","created_at":"2026-07-05T00:39:30.157676+00:00"},{"alias_kind":"pith_short_8","alias_value":"SCRZR534","created_at":"2026-07-05T00:39:30.157676+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1908.04901","citing_title":"On community structure in complex networks: challenges and opportunities","ref_index":61,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SCRZR534HSL5QOL3A5O5QZHZV6","json":"https://pith.science/pith/SCRZR534HSL5QOL3A5O5QZHZV6.json","graph_json":"https://pith.science/api/pith-number/SCRZR534HSL5QOL3A5O5QZHZV6/graph.json","events_json":"https://pith.science/api/pith-number/SCRZR534HSL5QOL3A5O5QZHZV6/events.json","paper":"https://pith.science/paper/SCRZR534"},"agent_actions":{"view_html":"https://pith.science/pith/SCRZR534HSL5QOL3A5O5QZHZV6","download_json":"https://pith.science/pith/SCRZR534HSL5QOL3A5O5QZHZV6.json","view_paper":"https://pith.science/paper/SCRZR534","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1808.06079&json=true","fetch_graph":"https://pith.science/api/pith-number/SCRZR534HSL5QOL3A5O5QZHZV6/graph.json","fetch_events":"https://pith.science/api/pith-number/SCRZR534HSL5QOL3A5O5QZHZV6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SCRZR534HSL5QOL3A5O5QZHZV6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SCRZR534HSL5QOL3A5O5QZHZV6/action/storage_attestation","attest_author":"https://pith.science/pith/SCRZR534HSL5QOL3A5O5QZHZV6/action/author_attestation","sign_citation":"https://pith.science/pith/SCRZR534HSL5QOL3A5O5QZHZV6/action/citation_signature","submit_replication":"https://pith.science/pith/SCRZR534HSL5QOL3A5O5QZHZV6/action/replication_record"}},"created_at":"2026-07-05T00:39:30.157676+00:00","updated_at":"2026-07-05T00:39:30.157676+00:00"}