{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:LE47QGI3M4IW3JNYETN2K3474Z","short_pith_number":"pith:LE47QGI3","canonical_record":{"source":{"id":"2412.06105","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-12-08T23:41:22Z","cross_cats_sorted":["cs.DC"],"title_canon_sha256":"42265f2860b35e3415839e7093a470311f0202ef483cacefc599d011a1986155","abstract_canon_sha256":"146dc3140976fa0b85d5ecb783b873528618f20d7e6079d4bdde1d2b7ee0dde7"},"schema_version":"1.0"},"canonical_sha256":"5939f8191b67116da5b824dba56f9fe66651aeb50cbd490878c22bce92e051fe","source":{"kind":"arxiv","id":"2412.06105","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.06105","created_at":"2026-07-05T09:46:15Z"},{"alias_kind":"arxiv_version","alias_value":"2412.06105v1","created_at":"2026-07-05T09:46:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.06105","created_at":"2026-07-05T09:46:15Z"},{"alias_kind":"pith_short_12","alias_value":"LE47QGI3M4IW","created_at":"2026-07-05T09:46:15Z"},{"alias_kind":"pith_short_16","alias_value":"LE47QGI3M4IW3JNY","created_at":"2026-07-05T09:46:15Z"},{"alias_kind":"pith_short_8","alias_value":"LE47QGI3","created_at":"2026-07-05T09:46:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:LE47QGI3M4IW3JNYETN2K3474Z","target":"record","payload":{"canonical_record":{"source":{"id":"2412.06105","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-12-08T23:41:22Z","cross_cats_sorted":["cs.DC"],"title_canon_sha256":"42265f2860b35e3415839e7093a470311f0202ef483cacefc599d011a1986155","abstract_canon_sha256":"146dc3140976fa0b85d5ecb783b873528618f20d7e6079d4bdde1d2b7ee0dde7"},"schema_version":"1.0"},"canonical_sha256":"5939f8191b67116da5b824dba56f9fe66651aeb50cbd490878c22bce92e051fe","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:46:15.108828Z","signature_b64":"bP8Rq/PwqglKdvcu87kkprcxG1ycp4CfPxN1b7LIErf9cqrY8Oej2MhvhDekTduEetZEy4P6Nk0TDkaHu2TtDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5939f8191b67116da5b824dba56f9fe66651aeb50cbd490878c22bce92e051fe","last_reissued_at":"2026-07-05T09:46:15.108441Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:46:15.108441Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2412.06105","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":"2026-07-05T09:46:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fupnmWpxhHGMgixePfySz3MiYnMEaC+yYuFYhZDTy7zkWe7a6FDjUdiO+9HHC2jdQSJ3nBCFSLia4Gia4E04AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T07:38:23.203609Z"},"content_sha256":"36013cf6135220a25e8372e9f68983c08a4ae4ed01d4ff404849a9252b396bd1","schema_version":"1.0","event_id":"sha256:36013cf6135220a25e8372e9f68983c08a4ae4ed01d4ff404849a9252b396bd1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:LE47QGI3M4IW3JNYETN2K3474Z","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Fully Distributed Online Training of Graph Neural Networks in Networked Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC"],"primary_cat":"cs.LG","authors_text":"Ananthram Swami, Gunjan Verma, Kevin Chan, Rostyslav Olshevskyi, Santiago Segarra, Zhongyuan Zhao","submitted_at":"2024-12-08T23:41:22Z","abstract_excerpt":"Graph neural networks (GNNs) are powerful tools for developing scalable, decentralized artificial intelligence in large-scale networked systems, such as wireless networks, power grids, and transportation networks. Currently, GNNs in networked systems mostly follow a paradigm of `centralized training, distributed execution', which limits their adaptability and slows down their development cycles. In this work, we fill this gap for the first time by developing a communication-efficient, fully distributed online training approach for GNNs applied to large networked systems. For a mini-batch with "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.06105","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/2412.06105/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":"2026-07-05T09:46:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Y2OKjukWLpkHFtvf2T88SQvOk8VBgaam1bdRCr1qfYCSQTEQjTxMEB5H3mHBJVwAUxdy5B4Gh6DN3n7mHwXkCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T07:38:23.204111Z"},"content_sha256":"3f5b908124930beb94cb03f4b370f24ef3084c3e89ac156c48cedfe2822eeb56","schema_version":"1.0","event_id":"sha256:3f5b908124930beb94cb03f4b370f24ef3084c3e89ac156c48cedfe2822eeb56"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LE47QGI3M4IW3JNYETN2K3474Z/bundle.json","state_url":"https://pith.science/pith/LE47QGI3M4IW3JNYETN2K3474Z/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LE47QGI3M4IW3JNYETN2K3474Z/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":"2026-08-07T07:38:23Z","links":{"resolver":"https://pith.science/pith/LE47QGI3M4IW3JNYETN2K3474Z","bundle":"https://pith.science/pith/LE47QGI3M4IW3JNYETN2K3474Z/bundle.json","state":"https://pith.science/pith/LE47QGI3M4IW3JNYETN2K3474Z/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LE47QGI3M4IW3JNYETN2K3474Z/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:LE47QGI3M4IW3JNYETN2K3474Z","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":"146dc3140976fa0b85d5ecb783b873528618f20d7e6079d4bdde1d2b7ee0dde7","cross_cats_sorted":["cs.DC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-12-08T23:41:22Z","title_canon_sha256":"42265f2860b35e3415839e7093a470311f0202ef483cacefc599d011a1986155"},"schema_version":"1.0","source":{"id":"2412.06105","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.06105","created_at":"2026-07-05T09:46:15Z"},{"alias_kind":"arxiv_version","alias_value":"2412.06105v1","created_at":"2026-07-05T09:46:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.06105","created_at":"2026-07-05T09:46:15Z"},{"alias_kind":"pith_short_12","alias_value":"LE47QGI3M4IW","created_at":"2026-07-05T09:46:15Z"},{"alias_kind":"pith_short_16","alias_value":"LE47QGI3M4IW3JNY","created_at":"2026-07-05T09:46:15Z"},{"alias_kind":"pith_short_8","alias_value":"LE47QGI3","created_at":"2026-07-05T09:46:15Z"}],"graph_snapshots":[{"event_id":"sha256:3f5b908124930beb94cb03f4b370f24ef3084c3e89ac156c48cedfe2822eeb56","target":"graph","created_at":"2026-07-05T09:46:15Z","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/2412.06105/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graph neural networks (GNNs) are powerful tools for developing scalable, decentralized artificial intelligence in large-scale networked systems, such as wireless networks, power grids, and transportation networks. Currently, GNNs in networked systems mostly follow a paradigm of `centralized training, distributed execution', which limits their adaptability and slows down their development cycles. In this work, we fill this gap for the first time by developing a communication-efficient, fully distributed online training approach for GNNs applied to large networked systems. For a mini-batch with ","authors_text":"Ananthram Swami, Gunjan Verma, Kevin Chan, Rostyslav Olshevskyi, Santiago Segarra, Zhongyuan Zhao","cross_cats":["cs.DC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-12-08T23:41:22Z","title":"Fully Distributed Online Training of Graph Neural Networks in Networked Systems"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.06105","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":"sha256:36013cf6135220a25e8372e9f68983c08a4ae4ed01d4ff404849a9252b396bd1","target":"record","created_at":"2026-07-05T09:46:15Z","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":"146dc3140976fa0b85d5ecb783b873528618f20d7e6079d4bdde1d2b7ee0dde7","cross_cats_sorted":["cs.DC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-12-08T23:41:22Z","title_canon_sha256":"42265f2860b35e3415839e7093a470311f0202ef483cacefc599d011a1986155"},"schema_version":"1.0","source":{"id":"2412.06105","kind":"arxiv","version":1}},"canonical_sha256":"5939f8191b67116da5b824dba56f9fe66651aeb50cbd490878c22bce92e051fe","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5939f8191b67116da5b824dba56f9fe66651aeb50cbd490878c22bce92e051fe","first_computed_at":"2026-07-05T09:46:15.108441Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:46:15.108441Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bP8Rq/PwqglKdvcu87kkprcxG1ycp4CfPxN1b7LIErf9cqrY8Oej2MhvhDekTduEetZEy4P6Nk0TDkaHu2TtDA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:46:15.108828Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.06105","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:36013cf6135220a25e8372e9f68983c08a4ae4ed01d4ff404849a9252b396bd1","sha256:3f5b908124930beb94cb03f4b370f24ef3084c3e89ac156c48cedfe2822eeb56"],"state_sha256":"b370ac463e51930fb9dfd65fb5460c5efe132ae2d8adb4921330748900f76666"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jSWSCMyTxKCY+DUBTz6IcdE0CWIX1COZv64OM0lfut0z5IBhR5bJmCj/yalRLx3GBhGhXfosQ9/hQCJ2mBpdAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T07:38:23.208285Z","bundle_sha256":"ee0907acaed41f71de5e5db576783c777fdb848a902512fd85c3e67111e28105"}}