{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:GIEMD6YOCGIUSCPJN2VGEEFAQI","short_pith_number":"pith:GIEMD6YO","canonical_record":{"source":{"id":"1902.10829","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DS","submitted_at":"2019-02-27T23:23:17Z","cross_cats_sorted":[],"title_canon_sha256":"7ad8aabed9acaa2b23802eef51d74ce1b1c434cd7e5f6754bee01ea60ca73d25","abstract_canon_sha256":"4b60c5f5aa9e824a886b9576f839d40083056fe46d3540a10076e66bcd52c5e4"},"schema_version":"1.0"},"canonical_sha256":"3208c1fb0e11914909e96eaa6210a0820ddcf0fe7c6d244e9a7e405b89152379","source":{"kind":"arxiv","id":"1902.10829","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1902.10829","created_at":"2026-05-17T23:42:42Z"},{"alias_kind":"arxiv_version","alias_value":"1902.10829v2","created_at":"2026-05-17T23:42:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.10829","created_at":"2026-05-17T23:42:42Z"},{"alias_kind":"pith_short_12","alias_value":"GIEMD6YOCGIU","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_16","alias_value":"GIEMD6YOCGIUSCPJ","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_8","alias_value":"GIEMD6YO","created_at":"2026-05-18T12:33:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:GIEMD6YOCGIUSCPJN2VGEEFAQI","target":"record","payload":{"canonical_record":{"source":{"id":"1902.10829","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DS","submitted_at":"2019-02-27T23:23:17Z","cross_cats_sorted":[],"title_canon_sha256":"7ad8aabed9acaa2b23802eef51d74ce1b1c434cd7e5f6754bee01ea60ca73d25","abstract_canon_sha256":"4b60c5f5aa9e824a886b9576f839d40083056fe46d3540a10076e66bcd52c5e4"},"schema_version":"1.0"},"canonical_sha256":"3208c1fb0e11914909e96eaa6210a0820ddcf0fe7c6d244e9a7e405b89152379","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:42:42.190697Z","signature_b64":"HpckvtZTBnMbYIhI0/8hgGUcdW56f9Lsao7Hm7/OEgacSxP+NkuOI4zVcglhXULtgxEmX+Le/R849H8pm8N9CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3208c1fb0e11914909e96eaa6210a0820ddcf0fe7c6d244e9a7e405b89152379","last_reissued_at":"2026-05-17T23:42:42.189915Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:42:42.189915Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1902.10829","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:42:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wXWKWbhS7oMY0guyUs/Q1B3ZX43stYF6rQ6ZVdC+9Gl6GYhswNsPZozXfCabd70LuVVrs2t2f2gQ8SN21f6sDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-01T15:35:06.030142Z"},"content_sha256":"c60d4af6c7aa6695f9f9e966092c00a9a5137fea54e53e7dba0ab13445e001cc","schema_version":"1.0","event_id":"sha256:c60d4af6c7aa6695f9f9e966092c00a9a5137fea54e53e7dba0ab13445e001cc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:GIEMD6YOCGIUSCPJN2VGEEFAQI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Improved algorithms for Correlation Clustering with local objectives","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DS","authors_text":"Konstantin Makarychev, Sanchit Kalhan, Timothy Zhou","submitted_at":"2019-02-27T23:23:17Z","abstract_excerpt":"Correlation Clustering is a powerful graph partitioning model that aims to cluster items based on the notion of similarity between items. An instance of the Correlation Clustering problem consists of a graph $G$ (not necessarily complete) whose edges are labeled by a binary classifier as ``similar'' and ``dissimilar''. An objective which has received a lot of attention in literature is that of minimizing the number of disagreements: an edge is in disagreement if it is a ``similar'' edge and is present across clusters or if it is a ``dissimilar'' edge and is present within a cluster. Define the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.10829","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":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:42:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8sPKfGdE+jsnT725LdmD1BAzvxOVu1UHX6muXBO1Hw8J3tWWy9xnMRuAtCOHy6UayIM7afxGja/u5PDpWb/FDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-01T15:35:06.030506Z"},"content_sha256":"5b1d4ce763497cc00568a7536faaff843ff1161b20d260d2851c2b56e3b64e93","schema_version":"1.0","event_id":"sha256:5b1d4ce763497cc00568a7536faaff843ff1161b20d260d2851c2b56e3b64e93"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GIEMD6YOCGIUSCPJN2VGEEFAQI/bundle.json","state_url":"https://pith.science/pith/GIEMD6YOCGIUSCPJN2VGEEFAQI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GIEMD6YOCGIUSCPJN2VGEEFAQI/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-01T15:35:06Z","links":{"resolver":"https://pith.science/pith/GIEMD6YOCGIUSCPJN2VGEEFAQI","bundle":"https://pith.science/pith/GIEMD6YOCGIUSCPJN2VGEEFAQI/bundle.json","state":"https://pith.science/pith/GIEMD6YOCGIUSCPJN2VGEEFAQI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GIEMD6YOCGIUSCPJN2VGEEFAQI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:GIEMD6YOCGIUSCPJN2VGEEFAQI","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"4b60c5f5aa9e824a886b9576f839d40083056fe46d3540a10076e66bcd52c5e4","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DS","submitted_at":"2019-02-27T23:23:17Z","title_canon_sha256":"7ad8aabed9acaa2b23802eef51d74ce1b1c434cd7e5f6754bee01ea60ca73d25"},"schema_version":"1.0","source":{"id":"1902.10829","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1902.10829","created_at":"2026-05-17T23:42:42Z"},{"alias_kind":"arxiv_version","alias_value":"1902.10829v2","created_at":"2026-05-17T23:42:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.10829","created_at":"2026-05-17T23:42:42Z"},{"alias_kind":"pith_short_12","alias_value":"GIEMD6YOCGIU","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_16","alias_value":"GIEMD6YOCGIUSCPJ","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_8","alias_value":"GIEMD6YO","created_at":"2026-05-18T12:33:18Z"}],"graph_snapshots":[{"event_id":"sha256:5b1d4ce763497cc00568a7536faaff843ff1161b20d260d2851c2b56e3b64e93","target":"graph","created_at":"2026-05-17T23:42:42Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Correlation Clustering is a powerful graph partitioning model that aims to cluster items based on the notion of similarity between items. An instance of the Correlation Clustering problem consists of a graph $G$ (not necessarily complete) whose edges are labeled by a binary classifier as ``similar'' and ``dissimilar''. An objective which has received a lot of attention in literature is that of minimizing the number of disagreements: an edge is in disagreement if it is a ``similar'' edge and is present across clusters or if it is a ``dissimilar'' edge and is present within a cluster. Define the","authors_text":"Konstantin Makarychev, Sanchit Kalhan, Timothy Zhou","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DS","submitted_at":"2019-02-27T23:23:17Z","title":"Improved algorithms for Correlation Clustering with local objectives"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.10829","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:c60d4af6c7aa6695f9f9e966092c00a9a5137fea54e53e7dba0ab13445e001cc","target":"record","created_at":"2026-05-17T23:42:42Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"4b60c5f5aa9e824a886b9576f839d40083056fe46d3540a10076e66bcd52c5e4","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DS","submitted_at":"2019-02-27T23:23:17Z","title_canon_sha256":"7ad8aabed9acaa2b23802eef51d74ce1b1c434cd7e5f6754bee01ea60ca73d25"},"schema_version":"1.0","source":{"id":"1902.10829","kind":"arxiv","version":2}},"canonical_sha256":"3208c1fb0e11914909e96eaa6210a0820ddcf0fe7c6d244e9a7e405b89152379","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3208c1fb0e11914909e96eaa6210a0820ddcf0fe7c6d244e9a7e405b89152379","first_computed_at":"2026-05-17T23:42:42.189915Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:42:42.189915Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HpckvtZTBnMbYIhI0/8hgGUcdW56f9Lsao7Hm7/OEgacSxP+NkuOI4zVcglhXULtgxEmX+Le/R849H8pm8N9CA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:42:42.190697Z","signed_message":"canonical_sha256_bytes"},"source_id":"1902.10829","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c60d4af6c7aa6695f9f9e966092c00a9a5137fea54e53e7dba0ab13445e001cc","sha256:5b1d4ce763497cc00568a7536faaff843ff1161b20d260d2851c2b56e3b64e93"],"state_sha256":"5177cf001a5f602228858e299d4fb58b5aedc791f550263cc6b24d2c99fff6ff"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tW4t0ZjS+2rBNPwAshaAGSnRyFZbS9pZ4F0VWzGtGmXX0u0qqYc1Y0ZZFEpvJ7W2D1+w8Hv3zvwSe3D2tdKIDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-01T15:35:06.032843Z","bundle_sha256":"2f0f4296f31fc7fe61a8942912e12fdc3e2ed257fd6c7bf3fb10cd649a786601"}}