{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:E63XTRDAALCIT5JTATQCXVDPAT","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":"4197eefec3078c7ce73b5818609d2af11b361535390dbf2638569eb529a9d2ca","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-09-05T16:10:20Z","title_canon_sha256":"9db489f8b7812cb5a648bda26bc96bc5d0a93f6f97b795e8456430788f951753"},"schema_version":"1.0","source":{"id":"2509.05207","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2509.05207","created_at":"2026-07-05T12:05:37Z"},{"alias_kind":"arxiv_version","alias_value":"2509.05207v1","created_at":"2026-07-05T12:05:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.05207","created_at":"2026-07-05T12:05:37Z"},{"alias_kind":"pith_short_12","alias_value":"E63XTRDAALCI","created_at":"2026-07-05T12:05:37Z"},{"alias_kind":"pith_short_16","alias_value":"E63XTRDAALCIT5JT","created_at":"2026-07-05T12:05:37Z"},{"alias_kind":"pith_short_8","alias_value":"E63XTRDA","created_at":"2026-07-05T12:05:37Z"}],"graph_snapshots":[{"event_id":"sha256:f63c8b33aa588ad7ad782c0808e4275e5a5116982e82a6e43ba3507643b34bb6","target":"graph","created_at":"2026-07-05T12:05:37Z","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/2509.05207/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graph Neural Networks (GNNs) have become popular across a diverse set of tasks in exploring structural relationships between entities. However, due to the highly connected structure of the datasets, distributed training of GNNs on large-scale graphs poses significant challenges. Traditional sampling-based approaches mitigate the computational loads, yet the communication overhead remains a challenge. This paper presents RapidGNN, a distributed GNN training framework with deterministic sampling-based scheduling to enable efficient cache construction and prefetching of remote features. Evaluatio","authors_text":"Arefin Niam, M S Q Zulkar Nine, Tevfik Kosar","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-09-05T16:10:20Z","title":"RapidGNN: Energy and Communication-Efficient Distributed Training on Large-Scale Graph Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.05207","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:d78d7bc482a624d68deafff0aeff1207ddc41f0f1d08a008d0eb46827c2d44a3","target":"record","created_at":"2026-07-05T12:05:37Z","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":"4197eefec3078c7ce73b5818609d2af11b361535390dbf2638569eb529a9d2ca","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-09-05T16:10:20Z","title_canon_sha256":"9db489f8b7812cb5a648bda26bc96bc5d0a93f6f97b795e8456430788f951753"},"schema_version":"1.0","source":{"id":"2509.05207","kind":"arxiv","version":1}},"canonical_sha256":"27b779c46002c489f53304e02bd46f04c8902b322388c897ef7ea6fa1dd5b5b0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"27b779c46002c489f53304e02bd46f04c8902b322388c897ef7ea6fa1dd5b5b0","first_computed_at":"2026-07-05T12:05:37.787654Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T12:05:37.787654Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"v1osLwd+K4Zdm9JLZMVQcBiSPo5hZeX401AFFHdOYAfFE6hVI0j//7LUbFETYS76Wcf2vWmueOZ2E0/zqNm7Cg==","signature_status":"signed_v1","signed_at":"2026-07-05T12:05:37.788156Z","signed_message":"canonical_sha256_bytes"},"source_id":"2509.05207","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d78d7bc482a624d68deafff0aeff1207ddc41f0f1d08a008d0eb46827c2d44a3","sha256:f63c8b33aa588ad7ad782c0808e4275e5a5116982e82a6e43ba3507643b34bb6"],"state_sha256":"a107531a48975ac4f3fc8babef9301543d7edae6b1aae5f48e28f9bdb3b4c08c"}