{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2012:EAXIMRTRUQ7NCZSEA5A3TRGX7W","short_pith_number":"pith:EAXIMRTR","schema_version":"1.0","canonical_sha256":"202e864671a43ed166440741b9c4d7fda309eef5a12865c465c002aca1e7162f","source":{"kind":"arxiv","id":"1201.2320","version":4},"attestation_state":"computed","paper":{"title":"Solving the minimum labelling spanning tree problem using intelligent optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DM","math.CO"],"primary_cat":"math.OC","authors_text":"Jose Andres Moreno-Perez, Nenad Mladenovic, Sergio Consoli","submitted_at":"2012-01-11T15:00:36Z","abstract_excerpt":"Given a connected, undirected graph whose edges are labelled (or coloured), the minimum labelling spanning tree (MLST) problem seeks a spanning tree whose edges have the smallest number of distinct labels (or colours). In recent work, the MLST problem has been shown to be NP-hard and some effective heuristics have been proposed and analyzed. In this paper we present an intelligent optimization algorithm to solve the problem. It is obtained by the basic Variable Neighbourhood Search heuristic with the integration of other complements from machine learning, statistics and experimental algorithmi"},"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":"1201.2320","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2012-01-11T15:00:36Z","cross_cats_sorted":["cs.DM","math.CO"],"title_canon_sha256":"e5e92ee239012a9cb4d37abf1c8e663748bb08f2f511fb9fb53419c1d8bc7c31","abstract_canon_sha256":"d028fa4b7b2e12f43ea35e93b2b0f0f0415a398513c470d09afe90150129f4ce"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:57:24.383446Z","signature_b64":"cvcOTimRJ0XQT8lOT1rTZMlz3ryCGpeVArnB5br/kw/hYj1N+gcmR+GWN8N2/ESDU+OywVZbKqBKvnyJicLuAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"202e864671a43ed166440741b9c4d7fda309eef5a12865c465c002aca1e7162f","last_reissued_at":"2026-05-18T02:57:24.382879Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:57:24.382879Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Solving the minimum labelling spanning tree problem using intelligent optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DM","math.CO"],"primary_cat":"math.OC","authors_text":"Jose Andres Moreno-Perez, Nenad Mladenovic, Sergio Consoli","submitted_at":"2012-01-11T15:00:36Z","abstract_excerpt":"Given a connected, undirected graph whose edges are labelled (or coloured), the minimum labelling spanning tree (MLST) problem seeks a spanning tree whose edges have the smallest number of distinct labels (or colours). In recent work, the MLST problem has been shown to be NP-hard and some effective heuristics have been proposed and analyzed. In this paper we present an intelligent optimization algorithm to solve the problem. It is obtained by the basic Variable Neighbourhood Search heuristic with the integration of other complements from machine learning, statistics and experimental algorithmi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1201.2320","kind":"arxiv","version":4},"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":"1201.2320","created_at":"2026-05-18T02:57:24.382968+00:00"},{"alias_kind":"arxiv_version","alias_value":"1201.2320v4","created_at":"2026-05-18T02:57:24.382968+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1201.2320","created_at":"2026-05-18T02:57:24.382968+00:00"},{"alias_kind":"pith_short_12","alias_value":"EAXIMRTRUQ7N","created_at":"2026-05-18T12:27:04.183437+00:00"},{"alias_kind":"pith_short_16","alias_value":"EAXIMRTRUQ7NCZSE","created_at":"2026-05-18T12:27:04.183437+00:00"},{"alias_kind":"pith_short_8","alias_value":"EAXIMRTR","created_at":"2026-05-18T12:27:04.183437+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/EAXIMRTRUQ7NCZSEA5A3TRGX7W","json":"https://pith.science/pith/EAXIMRTRUQ7NCZSEA5A3TRGX7W.json","graph_json":"https://pith.science/api/pith-number/EAXIMRTRUQ7NCZSEA5A3TRGX7W/graph.json","events_json":"https://pith.science/api/pith-number/EAXIMRTRUQ7NCZSEA5A3TRGX7W/events.json","paper":"https://pith.science/paper/EAXIMRTR"},"agent_actions":{"view_html":"https://pith.science/pith/EAXIMRTRUQ7NCZSEA5A3TRGX7W","download_json":"https://pith.science/pith/EAXIMRTRUQ7NCZSEA5A3TRGX7W.json","view_paper":"https://pith.science/paper/EAXIMRTR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1201.2320&json=true","fetch_graph":"https://pith.science/api/pith-number/EAXIMRTRUQ7NCZSEA5A3TRGX7W/graph.json","fetch_events":"https://pith.science/api/pith-number/EAXIMRTRUQ7NCZSEA5A3TRGX7W/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EAXIMRTRUQ7NCZSEA5A3TRGX7W/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EAXIMRTRUQ7NCZSEA5A3TRGX7W/action/storage_attestation","attest_author":"https://pith.science/pith/EAXIMRTRUQ7NCZSEA5A3TRGX7W/action/author_attestation","sign_citation":"https://pith.science/pith/EAXIMRTRUQ7NCZSEA5A3TRGX7W/action/citation_signature","submit_replication":"https://pith.science/pith/EAXIMRTRUQ7NCZSEA5A3TRGX7W/action/replication_record"}},"created_at":"2026-05-18T02:57:24.382968+00:00","updated_at":"2026-05-18T02:57:24.382968+00:00"}