{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:PJAB6F4ISOB7Q5DQMBKXAEDZIR","short_pith_number":"pith:PJAB6F4I","schema_version":"1.0","canonical_sha256":"7a401f17889383f87470605570107944706f184d5e8d78fda43e93329a65fab4","source":{"kind":"arxiv","id":"2005.02368","version":1},"attestation_state":"computed","paper":{"title":"Fast Dynamic Cuts, Distances and Effective Resistances via Vertex Sparsifiers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DS","authors_text":"Gramoz Goranci, Li Chen, Monika Henzinger, Richard Peng, Thatchaphol Saranurak","submitted_at":"2020-05-05T17:52:01Z","abstract_excerpt":"We present a general framework of designing efficient dynamic approximate algorithms for optimization on undirected graphs. In particular, we develop a technique that, given any problem that admits a certain notion of vertex sparsifiers, gives data structures that maintain approximate solutions in sub-linear update and query time. We illustrate the applicability of our paradigm to the following problems.\n  (1) A fully-dynamic algorithm that approximates all-pair maximum-flows/minimum-cuts up to a nearly logarithmic factor in $\\tilde{O}(n^{2/3})$ amortized time against an oblivious adversary, a"},"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":"2005.02368","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DS","submitted_at":"2020-05-05T17:52:01Z","cross_cats_sorted":[],"title_canon_sha256":"c4e29be21b9605662103bc94d7e2fb708bd0a7409a6200bca6d3faabdb0f238a","abstract_canon_sha256":"97dbe4905a58a0a81b612e7034563a2fb2d45645ef6f2473b1589b6d6d2fa41b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:00:25.220992Z","signature_b64":"qettHKWMIeVTYhZwzlvSAOOWnuJSJKUbIslDjG5tDw6Zx0XBkBOJ8QOkGlsDGfot2HYE/SrxF2CL0hg6hUVnAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7a401f17889383f87470605570107944706f184d5e8d78fda43e93329a65fab4","last_reissued_at":"2026-07-05T01:00:25.220601Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:00:25.220601Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fast Dynamic Cuts, Distances and Effective Resistances via Vertex Sparsifiers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DS","authors_text":"Gramoz Goranci, Li Chen, Monika Henzinger, Richard Peng, Thatchaphol Saranurak","submitted_at":"2020-05-05T17:52:01Z","abstract_excerpt":"We present a general framework of designing efficient dynamic approximate algorithms for optimization on undirected graphs. In particular, we develop a technique that, given any problem that admits a certain notion of vertex sparsifiers, gives data structures that maintain approximate solutions in sub-linear update and query time. We illustrate the applicability of our paradigm to the following problems.\n  (1) A fully-dynamic algorithm that approximates all-pair maximum-flows/minimum-cuts up to a nearly logarithmic factor in $\\tilde{O}(n^{2/3})$ amortized time against an oblivious adversary, a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2005.02368","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/2005.02368/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":"2005.02368","created_at":"2026-07-05T01:00:25.220651+00:00"},{"alias_kind":"arxiv_version","alias_value":"2005.02368v1","created_at":"2026-07-05T01:00:25.220651+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2005.02368","created_at":"2026-07-05T01:00:25.220651+00:00"},{"alias_kind":"pith_short_12","alias_value":"PJAB6F4ISOB7","created_at":"2026-07-05T01:00:25.220651+00:00"},{"alias_kind":"pith_short_16","alias_value":"PJAB6F4ISOB7Q5DQ","created_at":"2026-07-05T01:00:25.220651+00:00"},{"alias_kind":"pith_short_8","alias_value":"PJAB6F4I","created_at":"2026-07-05T01:00:25.220651+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.01809","citing_title":"A Near-Optimal Offline Algorithm for Dynamic All-Pairs Shortest Paths in Planar Digraphs","ref_index":10,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PJAB6F4ISOB7Q5DQMBKXAEDZIR","json":"https://pith.science/pith/PJAB6F4ISOB7Q5DQMBKXAEDZIR.json","graph_json":"https://pith.science/api/pith-number/PJAB6F4ISOB7Q5DQMBKXAEDZIR/graph.json","events_json":"https://pith.science/api/pith-number/PJAB6F4ISOB7Q5DQMBKXAEDZIR/events.json","paper":"https://pith.science/paper/PJAB6F4I"},"agent_actions":{"view_html":"https://pith.science/pith/PJAB6F4ISOB7Q5DQMBKXAEDZIR","download_json":"https://pith.science/pith/PJAB6F4ISOB7Q5DQMBKXAEDZIR.json","view_paper":"https://pith.science/paper/PJAB6F4I","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2005.02368&json=true","fetch_graph":"https://pith.science/api/pith-number/PJAB6F4ISOB7Q5DQMBKXAEDZIR/graph.json","fetch_events":"https://pith.science/api/pith-number/PJAB6F4ISOB7Q5DQMBKXAEDZIR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PJAB6F4ISOB7Q5DQMBKXAEDZIR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PJAB6F4ISOB7Q5DQMBKXAEDZIR/action/storage_attestation","attest_author":"https://pith.science/pith/PJAB6F4ISOB7Q5DQMBKXAEDZIR/action/author_attestation","sign_citation":"https://pith.science/pith/PJAB6F4ISOB7Q5DQMBKXAEDZIR/action/citation_signature","submit_replication":"https://pith.science/pith/PJAB6F4ISOB7Q5DQMBKXAEDZIR/action/replication_record"}},"created_at":"2026-07-05T01:00:25.220651+00:00","updated_at":"2026-07-05T01:00:25.220651+00:00"}