{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:NDH452SAFER4BXNYYASLBLAHGP","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":"2a3a463d9e9a3fac904c262b8eec83c2169edb68c73b22239a0bd685d58e580a","cross_cats_sorted":["cs.AR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-11-01T03:47:07Z","title_canon_sha256":"9145ca543b1dbeac66ba042e89a89ad5c93bdbbc64d0913b8db877f0d223baa0"},"schema_version":"1.0","source":{"id":"2111.00680","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2111.00680","created_at":"2026-07-05T04:48:37Z"},{"alias_kind":"arxiv_version","alias_value":"2111.00680v2","created_at":"2026-07-05T04:48:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.00680","created_at":"2026-07-05T04:48:37Z"},{"alias_kind":"pith_short_12","alias_value":"NDH452SAFER4","created_at":"2026-07-05T04:48:37Z"},{"alias_kind":"pith_short_16","alias_value":"NDH452SAFER4BXNY","created_at":"2026-07-05T04:48:37Z"},{"alias_kind":"pith_short_8","alias_value":"NDH452SA","created_at":"2026-07-05T04:48:37Z"}],"graph_snapshots":[{"event_id":"sha256:8df9946803cabe27a4ea2758e1696f65fddc54f8f6b7fda911e84cbd5e3ae19e","target":"graph","created_at":"2026-07-05T04:48:37Z","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/2111.00680/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recently, Graph Neural Networks (GNNs) have become state-of-the-art algorithms for analyzing non-euclidean graph data. However, to realize efficient GNN training is challenging, especially on large graphs. The reasons are many-folded: 1) GNN training incurs a substantial memory footprint. Full-batch training on large graphs even requires hundreds to thousands of gigabytes of memory. 2) GNN training involves both memory-intensive and computation-intensive operations, challenging current CPU/GPU platforms. 3) The irregularity of graphs can result in severe resource under-utilization and load-imb","authors_text":"Cong Li, Guangyu Sun, Xiaoyang Wang, Xuechao Wei, Zhe Zhou","cross_cats":["cs.AR"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-11-01T03:47:07Z","title":"GNNear: Accelerating Full-Batch Training of Graph Neural Networks with Near-Memory Processing"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.00680","kind":"arxiv","version":2},"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":"sha256:6566484e1c20552c00fd454a46bd17124627a952fa071934b3fe282db7dc8f13","target":"record","created_at":"2026-07-05T04:48:37Z","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":"2a3a463d9e9a3fac904c262b8eec83c2169edb68c73b22239a0bd685d58e580a","cross_cats_sorted":["cs.AR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-11-01T03:47:07Z","title_canon_sha256":"9145ca543b1dbeac66ba042e89a89ad5c93bdbbc64d0913b8db877f0d223baa0"},"schema_version":"1.0","source":{"id":"2111.00680","kind":"arxiv","version":2}},"canonical_sha256":"68cfceea402923c0ddb8c024b0ac0733dde228b21a7e3bc0acc00f6907c2a791","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"68cfceea402923c0ddb8c024b0ac0733dde228b21a7e3bc0acc00f6907c2a791","first_computed_at":"2026-07-05T04:48:37.682752Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:48:37.682752Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5bzOMGreV5/CLuEvPM4cU4ZLw7DEzeBM8xDKkKCztNEEoZsGRnKFFYHJbIRIzRrJeWnliBv3ZcEf9zx3bh6MCw==","signature_status":"signed_v1","signed_at":"2026-07-05T04:48:37.683184Z","signed_message":"canonical_sha256_bytes"},"source_id":"2111.00680","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6566484e1c20552c00fd454a46bd17124627a952fa071934b3fe282db7dc8f13","sha256:8df9946803cabe27a4ea2758e1696f65fddc54f8f6b7fda911e84cbd5e3ae19e"],"state_sha256":"c5498b6c1f0017f278c4f00676285045750d62a32dcc460de9f9810ccdf51ab9"}