{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2013:PFJWNJ6YNO2M2AJJSDZLI2UEJQ","short_pith_number":"pith:PFJWNJ6Y","canonical_record":{"source":{"id":"1310.4378","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","primary_cat":"physics.data-an","submitted_at":"2013-10-16T13:50:15Z","cross_cats_sorted":["cond-mat.stat-mech","cs.SI","physics.comp-ph","stat.ML"],"title_canon_sha256":"a2cc7a340f099479288832bfb111ec030be9e9907093a0ee275cdb957cc410a6","abstract_canon_sha256":"427dce22ed13d68216a1fe312510da15a9d76bcbf78b797eb3db9a10a638e695"},"schema_version":"1.0"},"canonical_sha256":"795366a7d86bb4cd012990f2b46a844c0e286cb480d2af6124c8d5b8b453fbbd","source":{"kind":"arxiv","id":"1310.4378","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1310.4378","created_at":"2026-05-18T03:02:34Z"},{"alias_kind":"arxiv_version","alias_value":"1310.4378v3","created_at":"2026-05-18T03:02:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1310.4378","created_at":"2026-05-18T03:02:34Z"},{"alias_kind":"pith_short_12","alias_value":"PFJWNJ6YNO2M","created_at":"2026-05-18T12:27:54Z"},{"alias_kind":"pith_short_16","alias_value":"PFJWNJ6YNO2M2AJJ","created_at":"2026-05-18T12:27:54Z"},{"alias_kind":"pith_short_8","alias_value":"PFJWNJ6Y","created_at":"2026-05-18T12:27:54Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2013:PFJWNJ6YNO2M2AJJSDZLI2UEJQ","target":"record","payload":{"canonical_record":{"source":{"id":"1310.4378","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","primary_cat":"physics.data-an","submitted_at":"2013-10-16T13:50:15Z","cross_cats_sorted":["cond-mat.stat-mech","cs.SI","physics.comp-ph","stat.ML"],"title_canon_sha256":"a2cc7a340f099479288832bfb111ec030be9e9907093a0ee275cdb957cc410a6","abstract_canon_sha256":"427dce22ed13d68216a1fe312510da15a9d76bcbf78b797eb3db9a10a638e695"},"schema_version":"1.0"},"canonical_sha256":"795366a7d86bb4cd012990f2b46a844c0e286cb480d2af6124c8d5b8b453fbbd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:02:34.232447Z","signature_b64":"AHywOj51jkO8c316DhOq+2UvqofUT8SVx88FOusJnhbieOixboHjn1rTU6evdKfyELpCroWIDalXXBiREsAhDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"795366a7d86bb4cd012990f2b46a844c0e286cb480d2af6124c8d5b8b453fbbd","last_reissued_at":"2026-05-18T03:02:34.231436Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:02:34.231436Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1310.4378","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:02:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dMmZd4A1D6TblQ4xJnMCFXED9NJZVBGhGOc7JknSt2DmRYje2XaDfZZDQHTaVXZuTZGzaqmShFqe9oWKaGNUCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T16:54:47.351260Z"},"content_sha256":"440805b0c170e45b693f931f561ac3ef46fc4a5a7af8dbca038cc1d8c102894b","schema_version":"1.0","event_id":"sha256:440805b0c170e45b693f931f561ac3ef46fc4a5a7af8dbca038cc1d8c102894b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2013:PFJWNJ6YNO2M2AJJSDZLI2UEJQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models","license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","headline":"","cross_cats":["cond-mat.stat-mech","cs.SI","physics.comp-ph","stat.ML"],"primary_cat":"physics.data-an","authors_text":"Tiago P. Peixoto","submitted_at":"2013-10-16T13:50:15Z","abstract_excerpt":"We present an efficient algorithm for the inference of stochastic block models in large networks. The algorithm can be used as an optimized Markov chain Monte Carlo (MCMC) method, with a fast mixing time and a much reduced susceptibility to getting trapped in metastable states, or as a greedy agglomerative heuristic, with an almost linear $O(N\\ln^2N)$ complexity, where $N$ is the number of nodes in the network, independent on the number of blocks being inferred. We show that the heuristic is capable of delivering results which are indistinguishable from the more exact and numerically expensive"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1310.4378","kind":"arxiv","version":3},"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":"2026-05-18T03:02:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IepXBQir6pL445UDqthDhvc3ifW86UJamKJ6n0jWQjctzwy1dSws9B0qH9CLy7tfyRyNpvutuTKgDqqX0tAYCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T16:54:47.351902Z"},"content_sha256":"1e324bc3bcfa8676dad5cb091f9c6b09219b7bfd73f16f80bc44f05094d6bdca","schema_version":"1.0","event_id":"sha256:1e324bc3bcfa8676dad5cb091f9c6b09219b7bfd73f16f80bc44f05094d6bdca"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PFJWNJ6YNO2M2AJJSDZLI2UEJQ/bundle.json","state_url":"https://pith.science/pith/PFJWNJ6YNO2M2AJJSDZLI2UEJQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PFJWNJ6YNO2M2AJJSDZLI2UEJQ/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":"2026-06-06T16:54:47Z","links":{"resolver":"https://pith.science/pith/PFJWNJ6YNO2M2AJJSDZLI2UEJQ","bundle":"https://pith.science/pith/PFJWNJ6YNO2M2AJJSDZLI2UEJQ/bundle.json","state":"https://pith.science/pith/PFJWNJ6YNO2M2AJJSDZLI2UEJQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PFJWNJ6YNO2M2AJJSDZLI2UEJQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2013:PFJWNJ6YNO2M2AJJSDZLI2UEJQ","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":"427dce22ed13d68216a1fe312510da15a9d76bcbf78b797eb3db9a10a638e695","cross_cats_sorted":["cond-mat.stat-mech","cs.SI","physics.comp-ph","stat.ML"],"license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","primary_cat":"physics.data-an","submitted_at":"2013-10-16T13:50:15Z","title_canon_sha256":"a2cc7a340f099479288832bfb111ec030be9e9907093a0ee275cdb957cc410a6"},"schema_version":"1.0","source":{"id":"1310.4378","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1310.4378","created_at":"2026-05-18T03:02:34Z"},{"alias_kind":"arxiv_version","alias_value":"1310.4378v3","created_at":"2026-05-18T03:02:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1310.4378","created_at":"2026-05-18T03:02:34Z"},{"alias_kind":"pith_short_12","alias_value":"PFJWNJ6YNO2M","created_at":"2026-05-18T12:27:54Z"},{"alias_kind":"pith_short_16","alias_value":"PFJWNJ6YNO2M2AJJ","created_at":"2026-05-18T12:27:54Z"},{"alias_kind":"pith_short_8","alias_value":"PFJWNJ6Y","created_at":"2026-05-18T12:27:54Z"}],"graph_snapshots":[{"event_id":"sha256:1e324bc3bcfa8676dad5cb091f9c6b09219b7bfd73f16f80bc44f05094d6bdca","target":"graph","created_at":"2026-05-18T03:02:34Z","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":"We present an efficient algorithm for the inference of stochastic block models in large networks. The algorithm can be used as an optimized Markov chain Monte Carlo (MCMC) method, with a fast mixing time and a much reduced susceptibility to getting trapped in metastable states, or as a greedy agglomerative heuristic, with an almost linear $O(N\\ln^2N)$ complexity, where $N$ is the number of nodes in the network, independent on the number of blocks being inferred. We show that the heuristic is capable of delivering results which are indistinguishable from the more exact and numerically expensive","authors_text":"Tiago P. Peixoto","cross_cats":["cond-mat.stat-mech","cs.SI","physics.comp-ph","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","primary_cat":"physics.data-an","submitted_at":"2013-10-16T13:50:15Z","title":"Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1310.4378","kind":"arxiv","version":3},"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":"sha256:440805b0c170e45b693f931f561ac3ef46fc4a5a7af8dbca038cc1d8c102894b","target":"record","created_at":"2026-05-18T03:02:34Z","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":"427dce22ed13d68216a1fe312510da15a9d76bcbf78b797eb3db9a10a638e695","cross_cats_sorted":["cond-mat.stat-mech","cs.SI","physics.comp-ph","stat.ML"],"license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","primary_cat":"physics.data-an","submitted_at":"2013-10-16T13:50:15Z","title_canon_sha256":"a2cc7a340f099479288832bfb111ec030be9e9907093a0ee275cdb957cc410a6"},"schema_version":"1.0","source":{"id":"1310.4378","kind":"arxiv","version":3}},"canonical_sha256":"795366a7d86bb4cd012990f2b46a844c0e286cb480d2af6124c8d5b8b453fbbd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"795366a7d86bb4cd012990f2b46a844c0e286cb480d2af6124c8d5b8b453fbbd","first_computed_at":"2026-05-18T03:02:34.231436Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:02:34.231436Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"AHywOj51jkO8c316DhOq+2UvqofUT8SVx88FOusJnhbieOixboHjn1rTU6evdKfyELpCroWIDalXXBiREsAhDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T03:02:34.232447Z","signed_message":"canonical_sha256_bytes"},"source_id":"1310.4378","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:440805b0c170e45b693f931f561ac3ef46fc4a5a7af8dbca038cc1d8c102894b","sha256:1e324bc3bcfa8676dad5cb091f9c6b09219b7bfd73f16f80bc44f05094d6bdca"],"state_sha256":"8f1f4ae9fce2cb2d16db4cd019e6c9f3c0b13dcf6838a0c60572ac0d53613937"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"r6tvQhHpq5D89vOWkRSGs4oIWDx22Df8FiTAudDlU+Lq17LtUqLBcuiimn2juGf6Nwrs/FTNs9sSkE6O9HrWBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-06T16:54:47.354808Z","bundle_sha256":"5c3e887df2d9bcef4d2b9402264c7921d53117d02641791aafd90a50148de0b0"}}