{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:R63VPFKKMWTC3QMGD3NKQKT2K4","short_pith_number":"pith:R63VPFKK","schema_version":"1.0","canonical_sha256":"8fb757954a65a62dc1861edaa82a7a570bcf6496fbded9fa4cbe14234697824d","source":{"kind":"arxiv","id":"2412.09968","version":1},"attestation_state":"computed","paper":{"title":"GraSP: Simple yet Effective Graph Similarity Predictions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Haoran Zheng, Jieming Shi, Renchi Yang","submitted_at":"2024-12-13T08:55:02Z","abstract_excerpt":"Graph similarity computation (GSC) is to calculate the similarity between one pair of graphs, which is a fundamental problem with fruitful applications in the graph community. In GSC, graph edit distance (GED) and maximum common subgraph (MCS) are two important similarity metrics, both of which are NP-hard to compute. Instead of calculating the exact values, recent solutions resort to leveraging graph neural networks (GNNs) to learn data-driven models for the estimation of GED and MCS. Most of them are built on components involving node-level interactions crossing graphs, which engender vast c"},"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":"2412.09968","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-12-13T08:55:02Z","cross_cats_sorted":[],"title_canon_sha256":"04b84525f67c81b5a2424db5083083ebb2f4c6c880aaf85a2cbae142f392feaf","abstract_canon_sha256":"86823d7f21e28cac6fc9ecae8ce044b58494469803f6f5191ee54ce6a527cf1f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:48:44.622178Z","signature_b64":"gfKR716YIvmQetI99QBPXwX/FD65B87f6XCj++Sc+2PunKqP22DbUtx5mRNT9gCdx7P61cnX58SHry/yupDmBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8fb757954a65a62dc1861edaa82a7a570bcf6496fbded9fa4cbe14234697824d","last_reissued_at":"2026-07-05T09:48:44.621724Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:48:44.621724Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GraSP: Simple yet Effective Graph Similarity Predictions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Haoran Zheng, Jieming Shi, Renchi Yang","submitted_at":"2024-12-13T08:55:02Z","abstract_excerpt":"Graph similarity computation (GSC) is to calculate the similarity between one pair of graphs, which is a fundamental problem with fruitful applications in the graph community. In GSC, graph edit distance (GED) and maximum common subgraph (MCS) are two important similarity metrics, both of which are NP-hard to compute. Instead of calculating the exact values, recent solutions resort to leveraging graph neural networks (GNNs) to learn data-driven models for the estimation of GED and MCS. Most of them are built on components involving node-level interactions crossing graphs, which engender vast c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.09968","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/2412.09968/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":"2412.09968","created_at":"2026-07-05T09:48:44.621781+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.09968v1","created_at":"2026-07-05T09:48:44.621781+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.09968","created_at":"2026-07-05T09:48:44.621781+00:00"},{"alias_kind":"pith_short_12","alias_value":"R63VPFKKMWTC","created_at":"2026-07-05T09:48:44.621781+00:00"},{"alias_kind":"pith_short_16","alias_value":"R63VPFKKMWTC3QMG","created_at":"2026-07-05T09:48:44.621781+00:00"},{"alias_kind":"pith_short_8","alias_value":"R63VPFKK","created_at":"2026-07-05T09:48:44.621781+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/R63VPFKKMWTC3QMGD3NKQKT2K4","json":"https://pith.science/pith/R63VPFKKMWTC3QMGD3NKQKT2K4.json","graph_json":"https://pith.science/api/pith-number/R63VPFKKMWTC3QMGD3NKQKT2K4/graph.json","events_json":"https://pith.science/api/pith-number/R63VPFKKMWTC3QMGD3NKQKT2K4/events.json","paper":"https://pith.science/paper/R63VPFKK"},"agent_actions":{"view_html":"https://pith.science/pith/R63VPFKKMWTC3QMGD3NKQKT2K4","download_json":"https://pith.science/pith/R63VPFKKMWTC3QMGD3NKQKT2K4.json","view_paper":"https://pith.science/paper/R63VPFKK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.09968&json=true","fetch_graph":"https://pith.science/api/pith-number/R63VPFKKMWTC3QMGD3NKQKT2K4/graph.json","fetch_events":"https://pith.science/api/pith-number/R63VPFKKMWTC3QMGD3NKQKT2K4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/R63VPFKKMWTC3QMGD3NKQKT2K4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/R63VPFKKMWTC3QMGD3NKQKT2K4/action/storage_attestation","attest_author":"https://pith.science/pith/R63VPFKKMWTC3QMGD3NKQKT2K4/action/author_attestation","sign_citation":"https://pith.science/pith/R63VPFKKMWTC3QMGD3NKQKT2K4/action/citation_signature","submit_replication":"https://pith.science/pith/R63VPFKKMWTC3QMGD3NKQKT2K4/action/replication_record"}},"created_at":"2026-07-05T09:48:44.621781+00:00","updated_at":"2026-07-05T09:48:44.621781+00:00"}