{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:FBVGLM3KWYGOQCHEV5G7FRT2GJ","short_pith_number":"pith:FBVGLM3K","schema_version":"1.0","canonical_sha256":"286a65b36ab60ce808e4af4df2c67a327ebbec0ab0baffc8926f1c85a876d504","source":{"kind":"arxiv","id":"2503.11910","version":1},"attestation_state":"computed","paper":{"title":"RTD-Lite: Scalable Topological Analysis for Comparing Weighted Graphs in Learning Tasks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","math.AT","math.SG"],"primary_cat":"cs.LG","authors_text":"Daria Voronkova, Eduard Tulchinskii, Evgeny Burnaev, Ilya Trofimov, Serguei Barannikov","submitted_at":"2025-03-14T22:42:13Z","abstract_excerpt":"Topological methods for comparing weighted graphs are valuable in various learning tasks but often suffer from computational inefficiency on large datasets. We introduce RTD-Lite, a scalable algorithm that efficiently compares topological features, specifically connectivity or cluster structures at arbitrary scales, of two weighted graphs with one-to-one correspondence between vertices. Using minimal spanning trees in auxiliary graphs, RTD-Lite captures topological discrepancies with $O(n^2)$ time and memory complexity. This efficiency enables its application in tasks like dimensionality reduc"},"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":"2503.11910","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-03-14T22:42:13Z","cross_cats_sorted":["cs.AI","math.AT","math.SG"],"title_canon_sha256":"d03a61d77b74e5de2746956c2d52a0cda05d0c53c63c680d4f8a16215de0f148","abstract_canon_sha256":"94209726c74912e50059f0568740e6f2b0813c8abf6d442e192838cae8f8c16c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-05T01:14:25.618938Z","signature_b64":"kDsl6UiCt9R6id3fFSk6Mvl+sQfckYbXGh1s8+dvqF3JpA9J6SCGe+poCDKZ0yjAgV9DuF+X53PrE6G/vFenCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"286a65b36ab60ce808e4af4df2c67a327ebbec0ab0baffc8926f1c85a876d504","last_reissued_at":"2026-06-05T01:14:25.618291Z","signature_status":"signed_v1","first_computed_at":"2026-06-05T01:14:25.618291Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RTD-Lite: Scalable Topological Analysis for Comparing Weighted Graphs in Learning Tasks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","math.AT","math.SG"],"primary_cat":"cs.LG","authors_text":"Daria Voronkova, Eduard Tulchinskii, Evgeny Burnaev, Ilya Trofimov, Serguei Barannikov","submitted_at":"2025-03-14T22:42:13Z","abstract_excerpt":"Topological methods for comparing weighted graphs are valuable in various learning tasks but often suffer from computational inefficiency on large datasets. We introduce RTD-Lite, a scalable algorithm that efficiently compares topological features, specifically connectivity or cluster structures at arbitrary scales, of two weighted graphs with one-to-one correspondence between vertices. Using minimal spanning trees in auxiliary graphs, RTD-Lite captures topological discrepancies with $O(n^2)$ time and memory complexity. This efficiency enables its application in tasks like dimensionality reduc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.11910","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/2503.11910/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":"2503.11910","created_at":"2026-06-05T01:14:25.618407+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.11910v1","created_at":"2026-06-05T01:14:25.618407+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.11910","created_at":"2026-06-05T01:14:25.618407+00:00"},{"alias_kind":"pith_short_12","alias_value":"FBVGLM3KWYGO","created_at":"2026-06-05T01:14:25.618407+00:00"},{"alias_kind":"pith_short_16","alias_value":"FBVGLM3KWYGOQCHE","created_at":"2026-06-05T01:14:25.618407+00:00"},{"alias_kind":"pith_short_8","alias_value":"FBVGLM3K","created_at":"2026-06-05T01:14:25.618407+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/FBVGLM3KWYGOQCHEV5G7FRT2GJ","json":"https://pith.science/pith/FBVGLM3KWYGOQCHEV5G7FRT2GJ.json","graph_json":"https://pith.science/api/pith-number/FBVGLM3KWYGOQCHEV5G7FRT2GJ/graph.json","events_json":"https://pith.science/api/pith-number/FBVGLM3KWYGOQCHEV5G7FRT2GJ/events.json","paper":"https://pith.science/paper/FBVGLM3K"},"agent_actions":{"view_html":"https://pith.science/pith/FBVGLM3KWYGOQCHEV5G7FRT2GJ","download_json":"https://pith.science/pith/FBVGLM3KWYGOQCHEV5G7FRT2GJ.json","view_paper":"https://pith.science/paper/FBVGLM3K","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.11910&json=true","fetch_graph":"https://pith.science/api/pith-number/FBVGLM3KWYGOQCHEV5G7FRT2GJ/graph.json","fetch_events":"https://pith.science/api/pith-number/FBVGLM3KWYGOQCHEV5G7FRT2GJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FBVGLM3KWYGOQCHEV5G7FRT2GJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FBVGLM3KWYGOQCHEV5G7FRT2GJ/action/storage_attestation","attest_author":"https://pith.science/pith/FBVGLM3KWYGOQCHEV5G7FRT2GJ/action/author_attestation","sign_citation":"https://pith.science/pith/FBVGLM3KWYGOQCHEV5G7FRT2GJ/action/citation_signature","submit_replication":"https://pith.science/pith/FBVGLM3KWYGOQCHEV5G7FRT2GJ/action/replication_record"}},"created_at":"2026-06-05T01:14:25.618407+00:00","updated_at":"2026-06-05T01:14:25.618407+00:00"}