{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:IRBOLWEJJN3VTU67PI26HEFKTG","short_pith_number":"pith:IRBOLWEJ","canonical_record":{"source":{"id":"1712.07568","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.PR","submitted_at":"2017-12-20T16:38:02Z","cross_cats_sorted":["cond-mat.stat-mech","math-ph","math.MP"],"title_canon_sha256":"70ce4dca86b33c3c9899ccb2c14b32587011b7461a8b6aef4e2beba49e35cc6e","abstract_canon_sha256":"e398accfdd9e2068b1efd7ed2f42e3d791d6ac679a876c289efc924eab5bf9d7"},"schema_version":"1.0"},"canonical_sha256":"4442e5d8894b7759d3df7a35e390aa998ca447ba7f7e348717491a08239064a8","source":{"kind":"arxiv","id":"1712.07568","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1712.07568","created_at":"2026-05-17T23:44:04Z"},{"alias_kind":"arxiv_version","alias_value":"1712.07568v1","created_at":"2026-05-17T23:44:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.07568","created_at":"2026-05-17T23:44:04Z"},{"alias_kind":"pith_short_12","alias_value":"IRBOLWEJJN3V","created_at":"2026-05-18T12:31:21Z"},{"alias_kind":"pith_short_16","alias_value":"IRBOLWEJJN3VTU67","created_at":"2026-05-18T12:31:21Z"},{"alias_kind":"pith_short_8","alias_value":"IRBOLWEJ","created_at":"2026-05-18T12:31:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:IRBOLWEJJN3VTU67PI26HEFKTG","target":"record","payload":{"canonical_record":{"source":{"id":"1712.07568","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.PR","submitted_at":"2017-12-20T16:38:02Z","cross_cats_sorted":["cond-mat.stat-mech","math-ph","math.MP"],"title_canon_sha256":"70ce4dca86b33c3c9899ccb2c14b32587011b7461a8b6aef4e2beba49e35cc6e","abstract_canon_sha256":"e398accfdd9e2068b1efd7ed2f42e3d791d6ac679a876c289efc924eab5bf9d7"},"schema_version":"1.0"},"canonical_sha256":"4442e5d8894b7759d3df7a35e390aa998ca447ba7f7e348717491a08239064a8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:44:04.776804Z","signature_b64":"2BtJpu2bFbtyeoN2oD2OwToJfMbwxWDMEYExCTCdfEnNj0QdzKwNXQKI1299Ry0iHEtsydd031bPQY+X6futDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4442e5d8894b7759d3df7a35e390aa998ca447ba7f7e348717491a08239064a8","last_reissued_at":"2026-05-17T23:44:04.776359Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:44:04.776359Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1712.07568","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:44:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PMXa1fcvp3XJXaxCHDhhEHo/ySizYhaTzMm6K8mPMj5s3zpuY25KnNnGRqtZze4AKCme/oHBuAvlPsokfyoSDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T05:44:57.834266Z"},"content_sha256":"e7070afdcc605781ea469c79ca1c60290427faaeb0a8640810111145a9f0c448","schema_version":"1.0","event_id":"sha256:e7070afdcc605781ea469c79ca1c60290427faaeb0a8640810111145a9f0c448"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:IRBOLWEJJN3VTU67PI26HEFKTG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Mixing Time of Vertex-Weighted Exponential Random Graphs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.stat-mech","math-ph","math.MP"],"primary_cat":"math.PR","authors_text":"Mei Yin, Ryan DeMuse, Terry Easlick","submitted_at":"2017-12-20T16:38:02Z","abstract_excerpt":"Exponential random graph models have become increasingly important in the study of modern networks ranging from social networks, economic networks, to biological networks. They seek to capture a wide variety of common network tendencies such as connectivity and reciprocity through local graph properties. Sampling from these exponential distributions is crucial for parameter estimation, hypothesis testing, as well as understanding the features of the network in question. We inspect the efficiency of a popular sampling technique, the Glauber dynamics, for vertex-weighted exponential random graph"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.07568","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:44:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"P35IFIkqNcHz8nkRjdr3jKX08fr6lm6JIPeP0+cudqbH2TBEnn8pVic8EKenQOn61maROw4e9a9R9H/xPmXCBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T05:44:57.834635Z"},"content_sha256":"b3804648ac4a115b72954e40d74d1a5e08a7f21711cc39389b5977691bbb75b5","schema_version":"1.0","event_id":"sha256:b3804648ac4a115b72954e40d74d1a5e08a7f21711cc39389b5977691bbb75b5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IRBOLWEJJN3VTU67PI26HEFKTG/bundle.json","state_url":"https://pith.science/pith/IRBOLWEJJN3VTU67PI26HEFKTG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IRBOLWEJJN3VTU67PI26HEFKTG/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":"2026-07-06T05:44:57Z","links":{"resolver":"https://pith.science/pith/IRBOLWEJJN3VTU67PI26HEFKTG","bundle":"https://pith.science/pith/IRBOLWEJJN3VTU67PI26HEFKTG/bundle.json","state":"https://pith.science/pith/IRBOLWEJJN3VTU67PI26HEFKTG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IRBOLWEJJN3VTU67PI26HEFKTG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:IRBOLWEJJN3VTU67PI26HEFKTG","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":"e398accfdd9e2068b1efd7ed2f42e3d791d6ac679a876c289efc924eab5bf9d7","cross_cats_sorted":["cond-mat.stat-mech","math-ph","math.MP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.PR","submitted_at":"2017-12-20T16:38:02Z","title_canon_sha256":"70ce4dca86b33c3c9899ccb2c14b32587011b7461a8b6aef4e2beba49e35cc6e"},"schema_version":"1.0","source":{"id":"1712.07568","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1712.07568","created_at":"2026-05-17T23:44:04Z"},{"alias_kind":"arxiv_version","alias_value":"1712.07568v1","created_at":"2026-05-17T23:44:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.07568","created_at":"2026-05-17T23:44:04Z"},{"alias_kind":"pith_short_12","alias_value":"IRBOLWEJJN3V","created_at":"2026-05-18T12:31:21Z"},{"alias_kind":"pith_short_16","alias_value":"IRBOLWEJJN3VTU67","created_at":"2026-05-18T12:31:21Z"},{"alias_kind":"pith_short_8","alias_value":"IRBOLWEJ","created_at":"2026-05-18T12:31:21Z"}],"graph_snapshots":[{"event_id":"sha256:b3804648ac4a115b72954e40d74d1a5e08a7f21711cc39389b5977691bbb75b5","target":"graph","created_at":"2026-05-17T23:44:04Z","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":"Exponential random graph models have become increasingly important in the study of modern networks ranging from social networks, economic networks, to biological networks. They seek to capture a wide variety of common network tendencies such as connectivity and reciprocity through local graph properties. Sampling from these exponential distributions is crucial for parameter estimation, hypothesis testing, as well as understanding the features of the network in question. We inspect the efficiency of a popular sampling technique, the Glauber dynamics, for vertex-weighted exponential random graph","authors_text":"Mei Yin, Ryan DeMuse, Terry Easlick","cross_cats":["cond-mat.stat-mech","math-ph","math.MP"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.PR","submitted_at":"2017-12-20T16:38:02Z","title":"Mixing Time of Vertex-Weighted Exponential Random Graphs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.07568","kind":"arxiv","version":1},"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:e7070afdcc605781ea469c79ca1c60290427faaeb0a8640810111145a9f0c448","target":"record","created_at":"2026-05-17T23:44:04Z","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":"e398accfdd9e2068b1efd7ed2f42e3d791d6ac679a876c289efc924eab5bf9d7","cross_cats_sorted":["cond-mat.stat-mech","math-ph","math.MP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.PR","submitted_at":"2017-12-20T16:38:02Z","title_canon_sha256":"70ce4dca86b33c3c9899ccb2c14b32587011b7461a8b6aef4e2beba49e35cc6e"},"schema_version":"1.0","source":{"id":"1712.07568","kind":"arxiv","version":1}},"canonical_sha256":"4442e5d8894b7759d3df7a35e390aa998ca447ba7f7e348717491a08239064a8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4442e5d8894b7759d3df7a35e390aa998ca447ba7f7e348717491a08239064a8","first_computed_at":"2026-05-17T23:44:04.776359Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:44:04.776359Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2BtJpu2bFbtyeoN2oD2OwToJfMbwxWDMEYExCTCdfEnNj0QdzKwNXQKI1299Ry0iHEtsydd031bPQY+X6futDw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:44:04.776804Z","signed_message":"canonical_sha256_bytes"},"source_id":"1712.07568","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e7070afdcc605781ea469c79ca1c60290427faaeb0a8640810111145a9f0c448","sha256:b3804648ac4a115b72954e40d74d1a5e08a7f21711cc39389b5977691bbb75b5"],"state_sha256":"3aedefa7a9b36885092ed912789469ce48c370cb48c60de327ef7419e587f673"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BdlC77EpPLUi4EAvsiUL/5INV0NhZBQtVkf7n1+SHWEDfWPW6HHO0CLIDkbnoqkTd3wQDyIqioSAZe6CYnS6DQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T05:44:57.836594Z","bundle_sha256":"daf9fd086c786dd2bbd0e002e7285662f96a37d10e2960149d716e64c9c37f0f"}}