{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:2YCGHXVEAO6NU3MXP5NZTYFEVP","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":"7e1bb4f8146b1a21991a985a70746e6a402110e8b25ce2afd667b2847567206a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-05-23T17:03:33Z","title_canon_sha256":"e562d77d2867f27798353bf824b344858f5a63ef5ccd4f75cc3a76aec3762d72"},"schema_version":"1.0","source":{"id":"1705.08415","kind":"arxiv","version":6}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.08415","created_at":"2026-07-05T01:25:33Z"},{"alias_kind":"arxiv_version","alias_value":"1705.08415v6","created_at":"2026-07-05T01:25:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.08415","created_at":"2026-07-05T01:25:33Z"},{"alias_kind":"pith_short_12","alias_value":"2YCGHXVEAO6N","created_at":"2026-07-05T01:25:33Z"},{"alias_kind":"pith_short_16","alias_value":"2YCGHXVEAO6NU3MX","created_at":"2026-07-05T01:25:33Z"},{"alias_kind":"pith_short_8","alias_value":"2YCGHXVE","created_at":"2026-07-05T01:25:33Z"}],"graph_snapshots":[{"event_id":"sha256:10db9349adbe88d3b10d619c54cb67dc3f18c8083d66ca8bbaa8467559722918","target":"graph","created_at":"2026-07-05T01:25:33Z","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/1705.08415/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Traditionally, community detection in graphs can be solved using spectral methods or posterior inference under probabilistic graphical models. Focusing on random graph families such as the stochastic block model, recent research has unified both approaches and identified both statistical and computational detection thresholds in terms of the signal-to-noise ratio. By recasting community detection as a node-wise classification problem on graphs, we can also study it from a learning perspective. We present a novel family of Graph Neural Networks (GNNs) for solving community detection problems in","authors_text":"Joan Bruna, Xiang Li, Zhengdao Chen","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-05-23T17:03:33Z","title":"Supervised Community Detection with Line Graph Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.08415","kind":"arxiv","version":6},"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":"sha256:2cdd17767b8d2f9c10c77bf1c265b1a855a7681fb752973c1e9dc118e1f024fa","target":"record","created_at":"2026-07-05T01:25:33Z","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":"7e1bb4f8146b1a21991a985a70746e6a402110e8b25ce2afd667b2847567206a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-05-23T17:03:33Z","title_canon_sha256":"e562d77d2867f27798353bf824b344858f5a63ef5ccd4f75cc3a76aec3762d72"},"schema_version":"1.0","source":{"id":"1705.08415","kind":"arxiv","version":6}},"canonical_sha256":"d60463dea403bcda6d977f5b99e0a4abf9f2be3a7138a9d5e29438f7a83e627c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d60463dea403bcda6d977f5b99e0a4abf9f2be3a7138a9d5e29438f7a83e627c","first_computed_at":"2026-07-05T01:25:33.110841Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:25:33.110841Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bdFeVKx4AxdQErf58rZ0D8wTZGIya6TijVY3uVIVR8ah+j0P64d8/vm6cuzQ+ABWa3dutP5f8XUjn9F3DQHwBg==","signature_status":"signed_v1","signed_at":"2026-07-05T01:25:33.111438Z","signed_message":"canonical_sha256_bytes"},"source_id":"1705.08415","source_kind":"arxiv","source_version":6}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2cdd17767b8d2f9c10c77bf1c265b1a855a7681fb752973c1e9dc118e1f024fa","sha256:10db9349adbe88d3b10d619c54cb67dc3f18c8083d66ca8bbaa8467559722918"],"state_sha256":"1c9c53584606f90ff8955683711ed2382648815584b15530e73318a54d1fc4e6"}