{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:5K4ZHCDRNHVDHVPKA5ZSJ2WXTB","short_pith_number":"pith:5K4ZHCDR","schema_version":"1.0","canonical_sha256":"eab993887169ea33d5ea077324ead79877a9ac61009b23108526c9761f525cfc","source":{"kind":"arxiv","id":"2306.00266","version":2},"attestation_state":"computed","paper":{"title":"A polynomial-time iterative algorithm for random graph matching with non-vanishing correlation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.PR","math.ST","stat.ML","stat.TH"],"primary_cat":"cs.DS","authors_text":"Jian Ding, Zhangsong Li","submitted_at":"2023-06-01T00:58:50Z","abstract_excerpt":"We propose an efficient algorithm for matching two correlated Erd\\H{o}s--R\\'enyi graphs with $n$ vertices whose edges are correlated through a latent vertex correspondence. When the edge density $q= n^{- \\alpha+o(1)}$ for a constant $\\alpha \\in [0,1)$, we show that our algorithm has polynomial running time and succeeds to recover the latent matching as long as the edge correlation is non-vanishing. This is closely related to our previous work on a polynomial-time algorithm that matches two Gaussian Wigner matrices with non-vanishing correlation, and provides the first polynomial-time random gr"},"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":"2306.00266","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DS","submitted_at":"2023-06-01T00:58:50Z","cross_cats_sorted":["math.PR","math.ST","stat.ML","stat.TH"],"title_canon_sha256":"1f109e02222b60dda65b3b684ecdf7fea76a48b101b3ef82ca7a567c0bcfed8a","abstract_canon_sha256":"4b7b5d08c16774a9776584716cdf53aee9945f229f2bd9f2a25762a72bc1dc16"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:52:40.433168Z","signature_b64":"hDlMGUVZnJPixkHYu24gExNzmim4HFmZ5ca7ignOehnvTn8jIHsxUqQfdY9cmt8pQHBdpjLsSBcixVlTuiliCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eab993887169ea33d5ea077324ead79877a9ac61009b23108526c9761f525cfc","last_reissued_at":"2026-07-05T07:52:40.432737Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:52:40.432737Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A polynomial-time iterative algorithm for random graph matching with non-vanishing correlation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.PR","math.ST","stat.ML","stat.TH"],"primary_cat":"cs.DS","authors_text":"Jian Ding, Zhangsong Li","submitted_at":"2023-06-01T00:58:50Z","abstract_excerpt":"We propose an efficient algorithm for matching two correlated Erd\\H{o}s--R\\'enyi graphs with $n$ vertices whose edges are correlated through a latent vertex correspondence. When the edge density $q= n^{- \\alpha+o(1)}$ for a constant $\\alpha \\in [0,1)$, we show that our algorithm has polynomial running time and succeeds to recover the latent matching as long as the edge correlation is non-vanishing. This is closely related to our previous work on a polynomial-time algorithm that matches two Gaussian Wigner matrices with non-vanishing correlation, and provides the first polynomial-time random gr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.00266","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/2306.00266/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":"2306.00266","created_at":"2026-07-05T07:52:40.432793+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.00266v2","created_at":"2026-07-05T07:52:40.432793+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.00266","created_at":"2026-07-05T07:52:40.432793+00:00"},{"alias_kind":"pith_short_12","alias_value":"5K4ZHCDRNHVD","created_at":"2026-07-05T07:52:40.432793+00:00"},{"alias_kind":"pith_short_16","alias_value":"5K4ZHCDRNHVDHVPK","created_at":"2026-07-05T07:52:40.432793+00:00"},{"alias_kind":"pith_short_8","alias_value":"5K4ZHCDR","created_at":"2026-07-05T07:52:40.432793+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2402.15095","citing_title":"The Umeyama algorithm for matching correlated Gaussian geometric models in the low-dimensional regime","ref_index":20,"is_internal_anchor":false},{"citing_arxiv_id":"2604.04365","citing_title":"Attributed Network Alignment: Statistical Limits and Efficient Algorithm","ref_index":5,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5K4ZHCDRNHVDHVPKA5ZSJ2WXTB","json":"https://pith.science/pith/5K4ZHCDRNHVDHVPKA5ZSJ2WXTB.json","graph_json":"https://pith.science/api/pith-number/5K4ZHCDRNHVDHVPKA5ZSJ2WXTB/graph.json","events_json":"https://pith.science/api/pith-number/5K4ZHCDRNHVDHVPKA5ZSJ2WXTB/events.json","paper":"https://pith.science/paper/5K4ZHCDR"},"agent_actions":{"view_html":"https://pith.science/pith/5K4ZHCDRNHVDHVPKA5ZSJ2WXTB","download_json":"https://pith.science/pith/5K4ZHCDRNHVDHVPKA5ZSJ2WXTB.json","view_paper":"https://pith.science/paper/5K4ZHCDR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.00266&json=true","fetch_graph":"https://pith.science/api/pith-number/5K4ZHCDRNHVDHVPKA5ZSJ2WXTB/graph.json","fetch_events":"https://pith.science/api/pith-number/5K4ZHCDRNHVDHVPKA5ZSJ2WXTB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5K4ZHCDRNHVDHVPKA5ZSJ2WXTB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5K4ZHCDRNHVDHVPKA5ZSJ2WXTB/action/storage_attestation","attest_author":"https://pith.science/pith/5K4ZHCDRNHVDHVPKA5ZSJ2WXTB/action/author_attestation","sign_citation":"https://pith.science/pith/5K4ZHCDRNHVDHVPKA5ZSJ2WXTB/action/citation_signature","submit_replication":"https://pith.science/pith/5K4ZHCDRNHVDHVPKA5ZSJ2WXTB/action/replication_record"}},"created_at":"2026-07-05T07:52:40.432793+00:00","updated_at":"2026-07-05T07:52:40.432793+00:00"}