{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:BSZZEMYPKD6MIAGTPAO6QTZ2NL","short_pith_number":"pith:BSZZEMYP","canonical_record":{"source":{"id":"2502.01810","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SI","submitted_at":"2025-02-03T20:41:06Z","cross_cats_sorted":["econ.EM","stat.CO","stat.ML"],"title_canon_sha256":"d2d33175762c3fb4f594b4789c3626f6e099b7a03781473cc3f42f89ffc82d59","abstract_canon_sha256":"d9a9937d2d50fbb7e019f25f08219977f012a18c804e81fbd7ca835824153ccf"},"schema_version":"1.0"},"canonical_sha256":"0cb392330f50fcc400d3781de84f3a6ac1bfc2812fc1d8f3e1ae9855991b7275","source":{"kind":"arxiv","id":"2502.01810","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.01810","created_at":"2026-07-05T10:09:19Z"},{"alias_kind":"arxiv_version","alias_value":"2502.01810v1","created_at":"2026-07-05T10:09:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.01810","created_at":"2026-07-05T10:09:19Z"},{"alias_kind":"pith_short_12","alias_value":"BSZZEMYPKD6M","created_at":"2026-07-05T10:09:19Z"},{"alias_kind":"pith_short_16","alias_value":"BSZZEMYPKD6MIAGT","created_at":"2026-07-05T10:09:19Z"},{"alias_kind":"pith_short_8","alias_value":"BSZZEMYP","created_at":"2026-07-05T10:09:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:BSZZEMYPKD6MIAGTPAO6QTZ2NL","target":"record","payload":{"canonical_record":{"source":{"id":"2502.01810","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SI","submitted_at":"2025-02-03T20:41:06Z","cross_cats_sorted":["econ.EM","stat.CO","stat.ML"],"title_canon_sha256":"d2d33175762c3fb4f594b4789c3626f6e099b7a03781473cc3f42f89ffc82d59","abstract_canon_sha256":"d9a9937d2d50fbb7e019f25f08219977f012a18c804e81fbd7ca835824153ccf"},"schema_version":"1.0"},"canonical_sha256":"0cb392330f50fcc400d3781de84f3a6ac1bfc2812fc1d8f3e1ae9855991b7275","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:09:19.608754Z","signature_b64":"Dw0VJI8Lj7NOLE94oVcQ8R6LyXVLkoPBvnqoGepwoyteOEiGvoRLGAc6VMTTbf3dbjTy/uT1UwcQxXzzH07NDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0cb392330f50fcc400d3781de84f3a6ac1bfc2812fc1d8f3e1ae9855991b7275","last_reissued_at":"2026-07-05T10:09:19.608236Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:09:19.608236Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2502.01810","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":"2026-07-05T10:09:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/FwGKdaG/puAG6st11t8f18wLhVxl88BijQ2t73hCHUc7IRnyRQ7cTHOi863FHewPzVDpIbRQSy5eGOjI0a5Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T21:18:20.965950Z"},"content_sha256":"fa34e16f49c226fdc65d7018c964268e796619ca5244ec8b0ab07f8a2f4dc4b1","schema_version":"1.0","event_id":"sha256:fa34e16f49c226fdc65d7018c964268e796619ca5244ec8b0ab07f8a2f4dc4b1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:BSZZEMYPKD6MIAGTPAO6QTZ2NL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Estimating Network Models using Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["econ.EM","stat.CO","stat.ML"],"primary_cat":"cs.SI","authors_text":"Angelo Mele","submitted_at":"2025-02-03T20:41:06Z","abstract_excerpt":"Exponential random graph models (ERGMs) are very flexible for modeling network formation but pose difficult estimation challenges due to their intractable normalizing constant. Existing methods, such as MCMC-MLE, rely on sequential simulation at every optimization step. We propose a neural network approach that trains on a single, large set of parameter-simulation pairs to learn the mapping from parameters to average network statistics. Once trained, this map can be inverted, yielding a fast and parallelizable estimation method. The procedure also accommodates extra network statistics to mitig"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.01810","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2502.01810/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T10:09:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DPjao1wPDJ9EL+iqLxOqJZVZlmU5kDcOUn1wByavJoj2En420sB46TPY86MSUGD2l7z8x6FzmcA0puUVnrWdBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T21:18:20.966499Z"},"content_sha256":"06e12ebd90854610d22791eb85499f73291709f6677604b5f57081229b290ae4","schema_version":"1.0","event_id":"sha256:06e12ebd90854610d22791eb85499f73291709f6677604b5f57081229b290ae4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BSZZEMYPKD6MIAGTPAO6QTZ2NL/bundle.json","state_url":"https://pith.science/pith/BSZZEMYPKD6MIAGTPAO6QTZ2NL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BSZZEMYPKD6MIAGTPAO6QTZ2NL/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":"2026-08-15T21:18:20Z","links":{"resolver":"https://pith.science/pith/BSZZEMYPKD6MIAGTPAO6QTZ2NL","bundle":"https://pith.science/pith/BSZZEMYPKD6MIAGTPAO6QTZ2NL/bundle.json","state":"https://pith.science/pith/BSZZEMYPKD6MIAGTPAO6QTZ2NL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BSZZEMYPKD6MIAGTPAO6QTZ2NL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:BSZZEMYPKD6MIAGTPAO6QTZ2NL","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":"d9a9937d2d50fbb7e019f25f08219977f012a18c804e81fbd7ca835824153ccf","cross_cats_sorted":["econ.EM","stat.CO","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SI","submitted_at":"2025-02-03T20:41:06Z","title_canon_sha256":"d2d33175762c3fb4f594b4789c3626f6e099b7a03781473cc3f42f89ffc82d59"},"schema_version":"1.0","source":{"id":"2502.01810","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.01810","created_at":"2026-07-05T10:09:19Z"},{"alias_kind":"arxiv_version","alias_value":"2502.01810v1","created_at":"2026-07-05T10:09:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.01810","created_at":"2026-07-05T10:09:19Z"},{"alias_kind":"pith_short_12","alias_value":"BSZZEMYPKD6M","created_at":"2026-07-05T10:09:19Z"},{"alias_kind":"pith_short_16","alias_value":"BSZZEMYPKD6MIAGT","created_at":"2026-07-05T10:09:19Z"},{"alias_kind":"pith_short_8","alias_value":"BSZZEMYP","created_at":"2026-07-05T10:09:19Z"}],"graph_snapshots":[{"event_id":"sha256:06e12ebd90854610d22791eb85499f73291709f6677604b5f57081229b290ae4","target":"graph","created_at":"2026-07-05T10:09:19Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2502.01810/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Exponential random graph models (ERGMs) are very flexible for modeling network formation but pose difficult estimation challenges due to their intractable normalizing constant. Existing methods, such as MCMC-MLE, rely on sequential simulation at every optimization step. We propose a neural network approach that trains on a single, large set of parameter-simulation pairs to learn the mapping from parameters to average network statistics. Once trained, this map can be inverted, yielding a fast and parallelizable estimation method. The procedure also accommodates extra network statistics to mitig","authors_text":"Angelo Mele","cross_cats":["econ.EM","stat.CO","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SI","submitted_at":"2025-02-03T20:41:06Z","title":"Estimating Network Models using Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.01810","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:fa34e16f49c226fdc65d7018c964268e796619ca5244ec8b0ab07f8a2f4dc4b1","target":"record","created_at":"2026-07-05T10:09:19Z","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":"d9a9937d2d50fbb7e019f25f08219977f012a18c804e81fbd7ca835824153ccf","cross_cats_sorted":["econ.EM","stat.CO","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SI","submitted_at":"2025-02-03T20:41:06Z","title_canon_sha256":"d2d33175762c3fb4f594b4789c3626f6e099b7a03781473cc3f42f89ffc82d59"},"schema_version":"1.0","source":{"id":"2502.01810","kind":"arxiv","version":1}},"canonical_sha256":"0cb392330f50fcc400d3781de84f3a6ac1bfc2812fc1d8f3e1ae9855991b7275","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0cb392330f50fcc400d3781de84f3a6ac1bfc2812fc1d8f3e1ae9855991b7275","first_computed_at":"2026-07-05T10:09:19.608236Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:09:19.608236Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Dw0VJI8Lj7NOLE94oVcQ8R6LyXVLkoPBvnqoGepwoyteOEiGvoRLGAc6VMTTbf3dbjTy/uT1UwcQxXzzH07NDg==","signature_status":"signed_v1","signed_at":"2026-07-05T10:09:19.608754Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.01810","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fa34e16f49c226fdc65d7018c964268e796619ca5244ec8b0ab07f8a2f4dc4b1","sha256:06e12ebd90854610d22791eb85499f73291709f6677604b5f57081229b290ae4"],"state_sha256":"f86d0cf2e22d3d30fa54fb1eacc34e5ce0a6fc4bb4010e9a8d50b1e7d0f5b82a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vrg36+7AHpZeG9oTdXik9idD01u7hlQWNVJIbXMouZ7Kd2mmML52WYTlGa9BNLzHrTGJ4s8/E2J7gJ+I5dHbCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-15T21:18:20.970172Z","bundle_sha256":"7caa5daba321d8528a5a7c5214939d4a73209fed06da7059efeb14da5beeb826"}}