{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:6IEEWTVMNATOS4ARPTW22QUZCS","short_pith_number":"pith:6IEEWTVM","schema_version":"1.0","canonical_sha256":"f2084b4eac6826e970117cedad42991491d55d9043e00f0bba928b121b824cda","source":{"kind":"arxiv","id":"1512.03306","version":1},"attestation_state":"computed","paper":{"title":"Toward more localized local algorithms: removing assumptions concerning global knowledge","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DS"],"primary_cat":"cs.DC","authors_text":"Amos Korman (GANG, Jean-S\\'ebastien Sereni (MASCOTTE), Laurent Viennot (GANG, LIAFA, LIAFA), LINCS)","submitted_at":"2015-12-10T16:14:59Z","abstract_excerpt":"Numerous sophisticated local algorithm were suggested in the literature for various fundamental problems. Notable examples are the MIS and $(\\Delta+1)$-coloring algorithms by Barenboim and Elkin [6], by Kuhn [22], and by Panconesi and Srinivasan [34], as well as the $O(\\Delta 2)$-coloring algorithm by Linial [28]. Unfortunately, most known local algorithms (including, in particular, the aforementioned algorithms) are non-uniform, that is, local algorithms generally use good estimations of one or more global parameters of the network, e.g., the maximum degree $\\Delta$ or the number of nodes n. "},"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":"1512.03306","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2015-12-10T16:14:59Z","cross_cats_sorted":["cs.DS"],"title_canon_sha256":"e171927cda5858d274985eaf4e1e9f417216339f00dba06c5802e4db10c72d43","abstract_canon_sha256":"d0b19eebc69a47679d6df174d8d432cdd08d6201d996c376efb78f3c2e689606"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:24:37.247039Z","signature_b64":"fjhxYUNtKcdY7Eyl0E542XcmpLhDRcNtgXlOK+JsEyEPHLTMgPtPDVB7qxL+3dg5nS1FtGLjDlkHhGExQe1ZCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f2084b4eac6826e970117cedad42991491d55d9043e00f0bba928b121b824cda","last_reissued_at":"2026-05-18T01:24:37.246607Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:24:37.246607Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Toward more localized local algorithms: removing assumptions concerning global knowledge","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DS"],"primary_cat":"cs.DC","authors_text":"Amos Korman (GANG, Jean-S\\'ebastien Sereni (MASCOTTE), Laurent Viennot (GANG, LIAFA, LIAFA), LINCS)","submitted_at":"2015-12-10T16:14:59Z","abstract_excerpt":"Numerous sophisticated local algorithm were suggested in the literature for various fundamental problems. Notable examples are the MIS and $(\\Delta+1)$-coloring algorithms by Barenboim and Elkin [6], by Kuhn [22], and by Panconesi and Srinivasan [34], as well as the $O(\\Delta 2)$-coloring algorithm by Linial [28]. Unfortunately, most known local algorithms (including, in particular, the aforementioned algorithms) are non-uniform, that is, local algorithms generally use good estimations of one or more global parameters of the network, e.g., the maximum degree $\\Delta$ or the number of nodes n. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1512.03306","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":"1512.03306","created_at":"2026-05-18T01:24:37.246684+00:00"},{"alias_kind":"arxiv_version","alias_value":"1512.03306v1","created_at":"2026-05-18T01:24:37.246684+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1512.03306","created_at":"2026-05-18T01:24:37.246684+00:00"},{"alias_kind":"pith_short_12","alias_value":"6IEEWTVMNATO","created_at":"2026-05-18T12:29:07.941421+00:00"},{"alias_kind":"pith_short_16","alias_value":"6IEEWTVMNATOS4AR","created_at":"2026-05-18T12:29:07.941421+00:00"},{"alias_kind":"pith_short_8","alias_value":"6IEEWTVM","created_at":"2026-05-18T12:29:07.941421+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/6IEEWTVMNATOS4ARPTW22QUZCS","json":"https://pith.science/pith/6IEEWTVMNATOS4ARPTW22QUZCS.json","graph_json":"https://pith.science/api/pith-number/6IEEWTVMNATOS4ARPTW22QUZCS/graph.json","events_json":"https://pith.science/api/pith-number/6IEEWTVMNATOS4ARPTW22QUZCS/events.json","paper":"https://pith.science/paper/6IEEWTVM"},"agent_actions":{"view_html":"https://pith.science/pith/6IEEWTVMNATOS4ARPTW22QUZCS","download_json":"https://pith.science/pith/6IEEWTVMNATOS4ARPTW22QUZCS.json","view_paper":"https://pith.science/paper/6IEEWTVM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1512.03306&json=true","fetch_graph":"https://pith.science/api/pith-number/6IEEWTVMNATOS4ARPTW22QUZCS/graph.json","fetch_events":"https://pith.science/api/pith-number/6IEEWTVMNATOS4ARPTW22QUZCS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6IEEWTVMNATOS4ARPTW22QUZCS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6IEEWTVMNATOS4ARPTW22QUZCS/action/storage_attestation","attest_author":"https://pith.science/pith/6IEEWTVMNATOS4ARPTW22QUZCS/action/author_attestation","sign_citation":"https://pith.science/pith/6IEEWTVMNATOS4ARPTW22QUZCS/action/citation_signature","submit_replication":"https://pith.science/pith/6IEEWTVMNATOS4ARPTW22QUZCS/action/replication_record"}},"created_at":"2026-05-18T01:24:37.246684+00:00","updated_at":"2026-05-18T01:24:37.246684+00:00"}