{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:S6TLI7CJAJ6Q5X6QWR5CKOC4UE","short_pith_number":"pith:S6TLI7CJ","schema_version":"1.0","canonical_sha256":"97a6b47c49027d0edfd0b47a25385ca11fdee87cb40a58eb4f9d725b1f0764f8","source":{"kind":"arxiv","id":"1506.05490","version":1},"attestation_state":"computed","paper":{"title":"Structural inference for uncertain networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.stat-mech","physics.soc-ph"],"primary_cat":"cs.SI","authors_text":"Brian Ball, M. E. J. Newman, Travis Martin","submitted_at":"2015-06-17T20:39:29Z","abstract_excerpt":"In the study of networked systems such as biological, technological, and social networks the available data are often uncertain. Rather than knowing the structure of a network exactly, we know the connections between nodes only with a certain probability. In this paper we develop methods for the analysis of such uncertain data, focusing particularly on the problem of community detection. We give a principled maximum-likelihood method for inferring community structure and demonstrate how the results can be used to make improved estimates of the true structure of the network. Using computer-gene"},"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":"1506.05490","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2015-06-17T20:39:29Z","cross_cats_sorted":["cond-mat.stat-mech","physics.soc-ph"],"title_canon_sha256":"859e4b49b0a164a80dfb842487b3c5ef93536b0564f82385c544296a33936563","abstract_canon_sha256":"d6582e218190b078de84d8f9c2b290a30d5341b3cf3f9699370632779feb8842"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:22:39.658544Z","signature_b64":"6MowYtnZWi5CX0Wk5knE9COm159nsrH8elBxYzzLZGQQUL4ZSC7zaqDxWL2cWAnji+P4nLKUAVsQjz5ePIlpDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"97a6b47c49027d0edfd0b47a25385ca11fdee87cb40a58eb4f9d725b1f0764f8","last_reissued_at":"2026-05-18T01:22:39.658136Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:22:39.658136Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Structural inference for uncertain networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.stat-mech","physics.soc-ph"],"primary_cat":"cs.SI","authors_text":"Brian Ball, M. E. J. Newman, Travis Martin","submitted_at":"2015-06-17T20:39:29Z","abstract_excerpt":"In the study of networked systems such as biological, technological, and social networks the available data are often uncertain. Rather than knowing the structure of a network exactly, we know the connections between nodes only with a certain probability. In this paper we develop methods for the analysis of such uncertain data, focusing particularly on the problem of community detection. We give a principled maximum-likelihood method for inferring community structure and demonstrate how the results can be used to make improved estimates of the true structure of the network. Using computer-gene"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1506.05490","kind":"arxiv","version":1},"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":"1506.05490","created_at":"2026-05-18T01:22:39.658196+00:00"},{"alias_kind":"arxiv_version","alias_value":"1506.05490v1","created_at":"2026-05-18T01:22:39.658196+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1506.05490","created_at":"2026-05-18T01:22:39.658196+00:00"},{"alias_kind":"pith_short_12","alias_value":"S6TLI7CJAJ6Q","created_at":"2026-05-18T12:29:39.896362+00:00"},{"alias_kind":"pith_short_16","alias_value":"S6TLI7CJAJ6Q5X6Q","created_at":"2026-05-18T12:29:39.896362+00:00"},{"alias_kind":"pith_short_8","alias_value":"S6TLI7CJ","created_at":"2026-05-18T12:29:39.896362+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/S6TLI7CJAJ6Q5X6QWR5CKOC4UE","json":"https://pith.science/pith/S6TLI7CJAJ6Q5X6QWR5CKOC4UE.json","graph_json":"https://pith.science/api/pith-number/S6TLI7CJAJ6Q5X6QWR5CKOC4UE/graph.json","events_json":"https://pith.science/api/pith-number/S6TLI7CJAJ6Q5X6QWR5CKOC4UE/events.json","paper":"https://pith.science/paper/S6TLI7CJ"},"agent_actions":{"view_html":"https://pith.science/pith/S6TLI7CJAJ6Q5X6QWR5CKOC4UE","download_json":"https://pith.science/pith/S6TLI7CJAJ6Q5X6QWR5CKOC4UE.json","view_paper":"https://pith.science/paper/S6TLI7CJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1506.05490&json=true","fetch_graph":"https://pith.science/api/pith-number/S6TLI7CJAJ6Q5X6QWR5CKOC4UE/graph.json","fetch_events":"https://pith.science/api/pith-number/S6TLI7CJAJ6Q5X6QWR5CKOC4UE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/S6TLI7CJAJ6Q5X6QWR5CKOC4UE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/S6TLI7CJAJ6Q5X6QWR5CKOC4UE/action/storage_attestation","attest_author":"https://pith.science/pith/S6TLI7CJAJ6Q5X6QWR5CKOC4UE/action/author_attestation","sign_citation":"https://pith.science/pith/S6TLI7CJAJ6Q5X6QWR5CKOC4UE/action/citation_signature","submit_replication":"https://pith.science/pith/S6TLI7CJAJ6Q5X6QWR5CKOC4UE/action/replication_record"}},"created_at":"2026-05-18T01:22:39.658196+00:00","updated_at":"2026-05-18T01:22:39.658196+00:00"}