{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:WMMEXCAUKFHVKAMBTKFONO6CIG","short_pith_number":"pith:WMMEXCAU","schema_version":"1.0","canonical_sha256":"b3184b8814514f5501819a8ae6bbc241ba010ab604b604f4bb4c2ac6c10249da","source":{"kind":"arxiv","id":"2506.01826","version":1},"attestation_state":"computed","paper":{"title":"Efficient Learning of Balanced Signed Graphs via Sparse Linear Programming","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"cs.LG","authors_text":"Gene Cheung, Haruki Yokota, Hiroshi Higashi, Yuichi Tanaka","submitted_at":"2025-06-02T16:09:51Z","abstract_excerpt":"Signed graphs are equipped with both positive and negative edge weights, encoding pairwise correlations as well as anti-correlations in data. A balanced signed graph is a signed graph with no cycles containing an odd number of negative edges. Laplacian of a balanced signed graph has eigenvectors that map via a simple linear transform to ones in a corresponding positive graph Laplacian, thus enabling reuse of spectral filtering tools designed for positive graphs. We propose an efficient method to learn a balanced signed graph Laplacian directly from data. Specifically, extending a previous line"},"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":"2506.01826","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-02T16:09:51Z","cross_cats_sorted":["eess.SP"],"title_canon_sha256":"30551e94fe4e19ab02bb0dee46f4e2397611b8a7abf438914c0f00f83a04bd7c","abstract_canon_sha256":"5391c7a7ca174c2fbcf6d3a4fbdd66b7f04b84e701233a7ae7ee516af06760c1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:14:19.971101Z","signature_b64":"sd3+CbJuWlhuJCm7sCYTrPLxykoSmLBjB3/bVStogXWE5eE5XOovBYwMkiBGXpkkTHtYnR/Sv3CR4fcM0DkSDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b3184b8814514f5501819a8ae6bbc241ba010ab604b604f4bb4c2ac6c10249da","last_reissued_at":"2026-07-05T11:14:19.970628Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:14:19.970628Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Efficient Learning of Balanced Signed Graphs via Sparse Linear Programming","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"cs.LG","authors_text":"Gene Cheung, Haruki Yokota, Hiroshi Higashi, Yuichi Tanaka","submitted_at":"2025-06-02T16:09:51Z","abstract_excerpt":"Signed graphs are equipped with both positive and negative edge weights, encoding pairwise correlations as well as anti-correlations in data. A balanced signed graph is a signed graph with no cycles containing an odd number of negative edges. Laplacian of a balanced signed graph has eigenvectors that map via a simple linear transform to ones in a corresponding positive graph Laplacian, thus enabling reuse of spectral filtering tools designed for positive graphs. We propose an efficient method to learn a balanced signed graph Laplacian directly from data. Specifically, extending a previous line"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.01826","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2506.01826/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":"2506.01826","created_at":"2026-07-05T11:14:19.970685+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.01826v1","created_at":"2026-07-05T11:14:19.970685+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.01826","created_at":"2026-07-05T11:14:19.970685+00:00"},{"alias_kind":"pith_short_12","alias_value":"WMMEXCAUKFHV","created_at":"2026-07-05T11:14:19.970685+00:00"},{"alias_kind":"pith_short_16","alias_value":"WMMEXCAUKFHVKAMB","created_at":"2026-07-05T11:14:19.970685+00:00"},{"alias_kind":"pith_short_8","alias_value":"WMMEXCAU","created_at":"2026-07-05T11:14:19.970685+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/WMMEXCAUKFHVKAMBTKFONO6CIG","json":"https://pith.science/pith/WMMEXCAUKFHVKAMBTKFONO6CIG.json","graph_json":"https://pith.science/api/pith-number/WMMEXCAUKFHVKAMBTKFONO6CIG/graph.json","events_json":"https://pith.science/api/pith-number/WMMEXCAUKFHVKAMBTKFONO6CIG/events.json","paper":"https://pith.science/paper/WMMEXCAU"},"agent_actions":{"view_html":"https://pith.science/pith/WMMEXCAUKFHVKAMBTKFONO6CIG","download_json":"https://pith.science/pith/WMMEXCAUKFHVKAMBTKFONO6CIG.json","view_paper":"https://pith.science/paper/WMMEXCAU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.01826&json=true","fetch_graph":"https://pith.science/api/pith-number/WMMEXCAUKFHVKAMBTKFONO6CIG/graph.json","fetch_events":"https://pith.science/api/pith-number/WMMEXCAUKFHVKAMBTKFONO6CIG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WMMEXCAUKFHVKAMBTKFONO6CIG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WMMEXCAUKFHVKAMBTKFONO6CIG/action/storage_attestation","attest_author":"https://pith.science/pith/WMMEXCAUKFHVKAMBTKFONO6CIG/action/author_attestation","sign_citation":"https://pith.science/pith/WMMEXCAUKFHVKAMBTKFONO6CIG/action/citation_signature","submit_replication":"https://pith.science/pith/WMMEXCAUKFHVKAMBTKFONO6CIG/action/replication_record"}},"created_at":"2026-07-05T11:14:19.970685+00:00","updated_at":"2026-07-05T11:14:19.970685+00:00"}