{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:UO5Y7BUSB5XVOZRD4TYUSAZ6FO","short_pith_number":"pith:UO5Y7BUS","schema_version":"1.0","canonical_sha256":"a3bb8f86920f6f576623e4f149033e2bba25e3b30403021e1710a5db8a968454","source":{"kind":"arxiv","id":"1906.11009","version":1},"attestation_state":"computed","paper":{"title":"Generalized Median Graph via Iterative Alternate Minimizations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["q-bio.QM"],"primary_cat":"cs.CV","authors_text":"Benoit Ga\\\"uz\\`ere (LITIS), Luc Brun, Nicolas Boria, S'ebastien Bougleux","submitted_at":"2019-06-26T12:04:55Z","abstract_excerpt":"Computing a graph prototype may constitute a core element for clustering or classification tasks. However, its computation is an NP-Hard problem, even for simple classes of graphs. In this paper, we propose an efficient approach based on block coordinate descent to compute a generalized median graph from a set of graphs. This approach relies on a clear definition of the optimization process and handles labeling on both edges and nodes. This iterative process optimizes the edit operations to perform on a graph alternatively on nodes and edges. Several experiments on different datasets show the "},"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":"1906.11009","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-26T12:04:55Z","cross_cats_sorted":["q-bio.QM"],"title_canon_sha256":"c4a3f9950caf505b60c763a9f8595b469eb0823e7815ed15d4e8cdd38ca4bc0b","abstract_canon_sha256":"776f20ff58583de2a08bdcfcb3e130daac79362c690ff9eb0dbbf9996f9956b8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:42:10.297339Z","signature_b64":"Wm8DGfQ5pDnjN5N3ltb52rqhNILrjhiwrlsWZtMT3JUh3OS3ayqz6XJhJq/tjLhTDtfwzE/VlEykko57QDHPDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a3bb8f86920f6f576623e4f149033e2bba25e3b30403021e1710a5db8a968454","last_reissued_at":"2026-05-17T23:42:10.296746Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:42:10.296746Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Generalized Median Graph via Iterative Alternate Minimizations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["q-bio.QM"],"primary_cat":"cs.CV","authors_text":"Benoit Ga\\\"uz\\`ere (LITIS), Luc Brun, Nicolas Boria, S'ebastien Bougleux","submitted_at":"2019-06-26T12:04:55Z","abstract_excerpt":"Computing a graph prototype may constitute a core element for clustering or classification tasks. However, its computation is an NP-Hard problem, even for simple classes of graphs. In this paper, we propose an efficient approach based on block coordinate descent to compute a generalized median graph from a set of graphs. This approach relies on a clear definition of the optimization process and handles labeling on both edges and nodes. This iterative process optimizes the edit operations to perform on a graph alternatively on nodes and edges. Several experiments on different datasets show the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.11009","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":""},"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":"1906.11009","created_at":"2026-05-17T23:42:10.296826+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.11009v1","created_at":"2026-05-17T23:42:10.296826+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.11009","created_at":"2026-05-17T23:42:10.296826+00:00"},{"alias_kind":"pith_short_12","alias_value":"UO5Y7BUSB5XV","created_at":"2026-05-18T12:33:30.264802+00:00"},{"alias_kind":"pith_short_16","alias_value":"UO5Y7BUSB5XVOZRD","created_at":"2026-05-18T12:33:30.264802+00:00"},{"alias_kind":"pith_short_8","alias_value":"UO5Y7BUS","created_at":"2026-05-18T12:33:30.264802+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/UO5Y7BUSB5XVOZRD4TYUSAZ6FO","json":"https://pith.science/pith/UO5Y7BUSB5XVOZRD4TYUSAZ6FO.json","graph_json":"https://pith.science/api/pith-number/UO5Y7BUSB5XVOZRD4TYUSAZ6FO/graph.json","events_json":"https://pith.science/api/pith-number/UO5Y7BUSB5XVOZRD4TYUSAZ6FO/events.json","paper":"https://pith.science/paper/UO5Y7BUS"},"agent_actions":{"view_html":"https://pith.science/pith/UO5Y7BUSB5XVOZRD4TYUSAZ6FO","download_json":"https://pith.science/pith/UO5Y7BUSB5XVOZRD4TYUSAZ6FO.json","view_paper":"https://pith.science/paper/UO5Y7BUS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.11009&json=true","fetch_graph":"https://pith.science/api/pith-number/UO5Y7BUSB5XVOZRD4TYUSAZ6FO/graph.json","fetch_events":"https://pith.science/api/pith-number/UO5Y7BUSB5XVOZRD4TYUSAZ6FO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UO5Y7BUSB5XVOZRD4TYUSAZ6FO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UO5Y7BUSB5XVOZRD4TYUSAZ6FO/action/storage_attestation","attest_author":"https://pith.science/pith/UO5Y7BUSB5XVOZRD4TYUSAZ6FO/action/author_attestation","sign_citation":"https://pith.science/pith/UO5Y7BUSB5XVOZRD4TYUSAZ6FO/action/citation_signature","submit_replication":"https://pith.science/pith/UO5Y7BUSB5XVOZRD4TYUSAZ6FO/action/replication_record"}},"created_at":"2026-05-17T23:42:10.296826+00:00","updated_at":"2026-05-17T23:42:10.296826+00:00"}