{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:Q264E5B5IPCVQUU75XR3TIVEXN","short_pith_number":"pith:Q264E5B5","schema_version":"1.0","canonical_sha256":"86bdc2743d43c558529fede3b9a2a4bb6aeaae9b175111597235b1b694c46b5c","source":{"kind":"arxiv","id":"1906.00699","version":2},"attestation_state":"computed","paper":{"title":"Evaluating network partitions through visualization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.soc-ph"],"primary_cat":"cs.SI","authors_text":"Chihiro Noguchi, Tatsuro Kawamoto","submitted_at":"2019-06-03T10:54:41Z","abstract_excerpt":"Network clustering requires making many decisions manually, such as the number of groups and a statistical model to be used. Even after filtering using an information criterion or regularizing with a nonparametric framework, we are commonly left with multiple candidates with reasonable partitions. In the end, the user has to decide which inferred groups should be regarded as informative. Here we propose a visualization method that efficiently represents network partitioning based on statistical inference algorithms. Our non-statistical assessment procedure based on visualization helps users ex"},"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":"1906.00699","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2019-06-03T10:54:41Z","cross_cats_sorted":["physics.soc-ph"],"title_canon_sha256":"fabd565afc54071dd44594da6fa5ce16a7820e4e24657f1869487fcc6c22066c","abstract_canon_sha256":"2dd83ca3be4414eff495dfc476d70c33db4c7ebb7841c74b6bc9f1f5006b3981"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:44:18.265186Z","signature_b64":"h1mU6CySQv61uhInlm/IIXNKNpasX69F2ibGe6+pOYdLYRJ9tuD68nxe4n3/+M0aN42hcIgyHycpBS89UT7uBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"86bdc2743d43c558529fede3b9a2a4bb6aeaae9b175111597235b1b694c46b5c","last_reissued_at":"2026-05-17T23:44:18.264570Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:44:18.264570Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Evaluating network partitions through visualization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.soc-ph"],"primary_cat":"cs.SI","authors_text":"Chihiro Noguchi, Tatsuro Kawamoto","submitted_at":"2019-06-03T10:54:41Z","abstract_excerpt":"Network clustering requires making many decisions manually, such as the number of groups and a statistical model to be used. Even after filtering using an information criterion or regularizing with a nonparametric framework, we are commonly left with multiple candidates with reasonable partitions. In the end, the user has to decide which inferred groups should be regarded as informative. Here we propose a visualization method that efficiently represents network partitioning based on statistical inference algorithms. Our non-statistical assessment procedure based on visualization helps users ex"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.00699","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":"1906.00699","created_at":"2026-05-17T23:44:18.264668+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.00699v2","created_at":"2026-05-17T23:44:18.264668+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.00699","created_at":"2026-05-17T23:44:18.264668+00:00"},{"alias_kind":"pith_short_12","alias_value":"Q264E5B5IPCV","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_16","alias_value":"Q264E5B5IPCVQUU7","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_8","alias_value":"Q264E5B5","created_at":"2026-05-18T12:33:24.271573+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1908.05976","citing_title":"HOTVis: Higher-Order Time-Aware Visualisation of Dynamic Graphs","ref_index":25,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Q264E5B5IPCVQUU75XR3TIVEXN","json":"https://pith.science/pith/Q264E5B5IPCVQUU75XR3TIVEXN.json","graph_json":"https://pith.science/api/pith-number/Q264E5B5IPCVQUU75XR3TIVEXN/graph.json","events_json":"https://pith.science/api/pith-number/Q264E5B5IPCVQUU75XR3TIVEXN/events.json","paper":"https://pith.science/paper/Q264E5B5"},"agent_actions":{"view_html":"https://pith.science/pith/Q264E5B5IPCVQUU75XR3TIVEXN","download_json":"https://pith.science/pith/Q264E5B5IPCVQUU75XR3TIVEXN.json","view_paper":"https://pith.science/paper/Q264E5B5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.00699&json=true","fetch_graph":"https://pith.science/api/pith-number/Q264E5B5IPCVQUU75XR3TIVEXN/graph.json","fetch_events":"https://pith.science/api/pith-number/Q264E5B5IPCVQUU75XR3TIVEXN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Q264E5B5IPCVQUU75XR3TIVEXN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Q264E5B5IPCVQUU75XR3TIVEXN/action/storage_attestation","attest_author":"https://pith.science/pith/Q264E5B5IPCVQUU75XR3TIVEXN/action/author_attestation","sign_citation":"https://pith.science/pith/Q264E5B5IPCVQUU75XR3TIVEXN/action/citation_signature","submit_replication":"https://pith.science/pith/Q264E5B5IPCVQUU75XR3TIVEXN/action/replication_record"}},"created_at":"2026-05-17T23:44:18.264668+00:00","updated_at":"2026-05-17T23:44:18.264668+00:00"}