{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:2TCMPGNRVZNW43SARXHHL6ABTA","short_pith_number":"pith:2TCMPGNR","schema_version":"1.0","canonical_sha256":"d4c4c799b1ae5b6e6e408dce75f80198030c589e8ee80d0923208ba15fa82145","source":{"kind":"arxiv","id":"2601.22943","version":2},"attestation_state":"computed","paper":{"title":"Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Guoren Wang, Hongchao Qin, Kangfei Zhao, Rong-Hua Li, Xiang Wu, Xunkai Li","submitted_at":"2026-01-30T13:02:26Z","abstract_excerpt":"Graph coarsening reduces the size of a graph while preserving certain properties. Most existing methods preserve either spectral or spatial characteristics. Recent research shows that topology-preserving coarsening methods maintain GNN performance on coarsened graphs but suffer from exponential time complexity. To address these problems, we propose Scalable Topology-Preserving Graph Coarsening (STPGC) by introducing the concepts of graph strong collapse and graph edge collapse extended from algebraic topology. STPGC comprises three new algorithms, GStrongCollapse, GEdgeCollapse, and Neighborho"},"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":"2601.22943","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-01-30T13:02:26Z","cross_cats_sorted":[],"title_canon_sha256":"4334f76307775a0ef68c98d8004e754a82152978257134509c7445b28350abe4","abstract_canon_sha256":"57d675ec9b3fbcce2fabc154d33877488becfe2737920cb5504268711fb2ab5d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-01T01:03:49.244158Z","signature_b64":"/ZhZ8Xi9DoWuJmbKpW5MTvspOHdWTc+mqntEXvxZ9/GYbVh9czAXSGPBr2sBpr5DckAQyaNiZA/uDATkCLfjDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d4c4c799b1ae5b6e6e408dce75f80198030c589e8ee80d0923208ba15fa82145","last_reissued_at":"2026-06-01T01:03:49.243339Z","signature_status":"signed_v1","first_computed_at":"2026-06-01T01:03:49.243339Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Guoren Wang, Hongchao Qin, Kangfei Zhao, Rong-Hua Li, Xiang Wu, Xunkai Li","submitted_at":"2026-01-30T13:02:26Z","abstract_excerpt":"Graph coarsening reduces the size of a graph while preserving certain properties. Most existing methods preserve either spectral or spatial characteristics. Recent research shows that topology-preserving coarsening methods maintain GNN performance on coarsened graphs but suffer from exponential time complexity. To address these problems, we propose Scalable Topology-Preserving Graph Coarsening (STPGC) by introducing the concepts of graph strong collapse and graph edge collapse extended from algebraic topology. STPGC comprises three new algorithms, GStrongCollapse, GEdgeCollapse, and Neighborho"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.22943","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/2601.22943/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":"2601.22943","created_at":"2026-06-01T01:03:49.243456+00:00"},{"alias_kind":"arxiv_version","alias_value":"2601.22943v2","created_at":"2026-06-01T01:03:49.243456+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.22943","created_at":"2026-06-01T01:03:49.243456+00:00"},{"alias_kind":"pith_short_12","alias_value":"2TCMPGNRVZNW","created_at":"2026-06-01T01:03:49.243456+00:00"},{"alias_kind":"pith_short_16","alias_value":"2TCMPGNRVZNW43SA","created_at":"2026-06-01T01:03:49.243456+00:00"},{"alias_kind":"pith_short_8","alias_value":"2TCMPGNR","created_at":"2026-06-01T01:03:49.243456+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/2TCMPGNRVZNW43SARXHHL6ABTA","json":"https://pith.science/pith/2TCMPGNRVZNW43SARXHHL6ABTA.json","graph_json":"https://pith.science/api/pith-number/2TCMPGNRVZNW43SARXHHL6ABTA/graph.json","events_json":"https://pith.science/api/pith-number/2TCMPGNRVZNW43SARXHHL6ABTA/events.json","paper":"https://pith.science/paper/2TCMPGNR"},"agent_actions":{"view_html":"https://pith.science/pith/2TCMPGNRVZNW43SARXHHL6ABTA","download_json":"https://pith.science/pith/2TCMPGNRVZNW43SARXHHL6ABTA.json","view_paper":"https://pith.science/paper/2TCMPGNR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2601.22943&json=true","fetch_graph":"https://pith.science/api/pith-number/2TCMPGNRVZNW43SARXHHL6ABTA/graph.json","fetch_events":"https://pith.science/api/pith-number/2TCMPGNRVZNW43SARXHHL6ABTA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2TCMPGNRVZNW43SARXHHL6ABTA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2TCMPGNRVZNW43SARXHHL6ABTA/action/storage_attestation","attest_author":"https://pith.science/pith/2TCMPGNRVZNW43SARXHHL6ABTA/action/author_attestation","sign_citation":"https://pith.science/pith/2TCMPGNRVZNW43SARXHHL6ABTA/action/citation_signature","submit_replication":"https://pith.science/pith/2TCMPGNRVZNW43SARXHHL6ABTA/action/replication_record"}},"created_at":"2026-06-01T01:03:49.243456+00:00","updated_at":"2026-06-01T01:03:49.243456+00:00"}