{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:EPO2YKVDHDJO7SE7F54GE24URN","short_pith_number":"pith:EPO2YKVD","canonical_record":{"source":{"id":"1810.08403","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2018-10-19T08:50:58Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"befce75957e90d22d1ecce752b0e024d523671af484e4dec504d5dfe5bbef96a","abstract_canon_sha256":"e3e024aef2c80f565847b93f9acd2802d5b4a37485beaf382a4d0a796cfc0d5a"},"schema_version":"1.0"},"canonical_sha256":"23ddac2aa338d2efc89f2f78626b948b5967d03c9cc996514f73147cf99e02f8","source":{"kind":"arxiv","id":"1810.08403","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.08403","created_at":"2026-05-18T00:02:48Z"},{"alias_kind":"arxiv_version","alias_value":"1810.08403v1","created_at":"2026-05-18T00:02:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.08403","created_at":"2026-05-18T00:02:48Z"},{"alias_kind":"pith_short_12","alias_value":"EPO2YKVDHDJO","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_16","alias_value":"EPO2YKVDHDJO7SE7","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_8","alias_value":"EPO2YKVD","created_at":"2026-05-18T12:32:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:EPO2YKVDHDJO7SE7F54GE24URN","target":"record","payload":{"canonical_record":{"source":{"id":"1810.08403","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2018-10-19T08:50:58Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"befce75957e90d22d1ecce752b0e024d523671af484e4dec504d5dfe5bbef96a","abstract_canon_sha256":"e3e024aef2c80f565847b93f9acd2802d5b4a37485beaf382a4d0a796cfc0d5a"},"schema_version":"1.0"},"canonical_sha256":"23ddac2aa338d2efc89f2f78626b948b5967d03c9cc996514f73147cf99e02f8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:02:48.206020Z","signature_b64":"M+M1o45GJoT/F2IwHjMukb+33oR6KYTwfkIQUC74Z3Wn8WIQVNJU4plVTECqwvhx0NO8XGAwavWZLYres+9MAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"23ddac2aa338d2efc89f2f78626b948b5967d03c9cc996514f73147cf99e02f8","last_reissued_at":"2026-05-18T00:02:48.205377Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:02:48.205377Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1810.08403","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":"2026-05-18T00:02:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"x2ybJCC2oJA/9FD7W1m/vUJSaNCVnOc5dyEXLnFqzeIkXw0hQa0+9CpZXUZaIz9qQFdJc9b+e3+yJFAx7UY9Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T02:24:12.472573Z"},"content_sha256":"bf20f059e6f3dfd5e68defd9ab8b6d8497ee25e378fee940b3ae78fbf608c710","schema_version":"1.0","event_id":"sha256:bf20f059e6f3dfd5e68defd9ab8b6d8497ee25e378fee940b3ae78fbf608c710"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:EPO2YKVDHDJO7SE7F54GE24URN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Towards Efficient Large-Scale Graph Neural Network Computing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.DC","authors_text":"Jilong Xue, Lidong Zhou, Lingxiao Ma, Ming Wu, Yafei Dai, Youshan Miao, Zhi Yang","submitted_at":"2018-10-19T08:50:58Z","abstract_excerpt":"Recent deep learning models have moved beyond low-dimensional regular grids such as image, video, and speech, to high-dimensional graph-structured data, such as social networks, brain connections, and knowledge graphs. This evolution has led to large graph-based irregular and sparse models that go beyond what existing deep learning frameworks are designed for. Further, these models are not easily amenable to efficient, at scale, acceleration on parallel hardwares (e.g. GPUs). We introduce NGra, the first parallel processing framework for graph-based deep neural networks (GNNs). NGra presents a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.08403","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":""},"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":"2026-05-18T00:02:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JYNDVmE7CHsskz4qetLJ+Geh3tmGO5KD3Y4TdAhjmrSVm2mQmRMYykxnWS7YXDp94jAF2o/RO/qAftnfUnyMCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T02:24:12.473209Z"},"content_sha256":"86e03831d86c6bdce86839a74391f825e0ba7275930c05967dae10f9ab2019bb","schema_version":"1.0","event_id":"sha256:86e03831d86c6bdce86839a74391f825e0ba7275930c05967dae10f9ab2019bb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EPO2YKVDHDJO7SE7F54GE24URN/bundle.json","state_url":"https://pith.science/pith/EPO2YKVDHDJO7SE7F54GE24URN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EPO2YKVDHDJO7SE7F54GE24URN/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":"2026-05-26T02:24:12Z","links":{"resolver":"https://pith.science/pith/EPO2YKVDHDJO7SE7F54GE24URN","bundle":"https://pith.science/pith/EPO2YKVDHDJO7SE7F54GE24URN/bundle.json","state":"https://pith.science/pith/EPO2YKVDHDJO7SE7F54GE24URN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EPO2YKVDHDJO7SE7F54GE24URN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:EPO2YKVDHDJO7SE7F54GE24URN","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":"e3e024aef2c80f565847b93f9acd2802d5b4a37485beaf382a4d0a796cfc0d5a","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2018-10-19T08:50:58Z","title_canon_sha256":"befce75957e90d22d1ecce752b0e024d523671af484e4dec504d5dfe5bbef96a"},"schema_version":"1.0","source":{"id":"1810.08403","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.08403","created_at":"2026-05-18T00:02:48Z"},{"alias_kind":"arxiv_version","alias_value":"1810.08403v1","created_at":"2026-05-18T00:02:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.08403","created_at":"2026-05-18T00:02:48Z"},{"alias_kind":"pith_short_12","alias_value":"EPO2YKVDHDJO","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_16","alias_value":"EPO2YKVDHDJO7SE7","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_8","alias_value":"EPO2YKVD","created_at":"2026-05-18T12:32:22Z"}],"graph_snapshots":[{"event_id":"sha256:86e03831d86c6bdce86839a74391f825e0ba7275930c05967dae10f9ab2019bb","target":"graph","created_at":"2026-05-18T00:02:48Z","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"},"paper":{"abstract_excerpt":"Recent deep learning models have moved beyond low-dimensional regular grids such as image, video, and speech, to high-dimensional graph-structured data, such as social networks, brain connections, and knowledge graphs. This evolution has led to large graph-based irregular and sparse models that go beyond what existing deep learning frameworks are designed for. Further, these models are not easily amenable to efficient, at scale, acceleration on parallel hardwares (e.g. GPUs). We introduce NGra, the first parallel processing framework for graph-based deep neural networks (GNNs). NGra presents a","authors_text":"Jilong Xue, Lidong Zhou, Lingxiao Ma, Ming Wu, Yafei Dai, Youshan Miao, Zhi Yang","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2018-10-19T08:50:58Z","title":"Towards Efficient Large-Scale Graph Neural Network Computing"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.08403","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:bf20f059e6f3dfd5e68defd9ab8b6d8497ee25e378fee940b3ae78fbf608c710","target":"record","created_at":"2026-05-18T00:02:48Z","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":"e3e024aef2c80f565847b93f9acd2802d5b4a37485beaf382a4d0a796cfc0d5a","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2018-10-19T08:50:58Z","title_canon_sha256":"befce75957e90d22d1ecce752b0e024d523671af484e4dec504d5dfe5bbef96a"},"schema_version":"1.0","source":{"id":"1810.08403","kind":"arxiv","version":1}},"canonical_sha256":"23ddac2aa338d2efc89f2f78626b948b5967d03c9cc996514f73147cf99e02f8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"23ddac2aa338d2efc89f2f78626b948b5967d03c9cc996514f73147cf99e02f8","first_computed_at":"2026-05-18T00:02:48.205377Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:02:48.205377Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"M+M1o45GJoT/F2IwHjMukb+33oR6KYTwfkIQUC74Z3Wn8WIQVNJU4plVTECqwvhx0NO8XGAwavWZLYres+9MAA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:02:48.206020Z","signed_message":"canonical_sha256_bytes"},"source_id":"1810.08403","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bf20f059e6f3dfd5e68defd9ab8b6d8497ee25e378fee940b3ae78fbf608c710","sha256:86e03831d86c6bdce86839a74391f825e0ba7275930c05967dae10f9ab2019bb"],"state_sha256":"5a62a4766de7d7a5a6c0b20b719b13b95ad28ed9844e6dd279e06daa79305684"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kusiPWD/KQKnrlfR2/NNRs+gUlW9KDU9R3KdW3n9Wq/SnUhUik8/66vhLIQKk0zhwWMnciBTLcgQKsK75JgOCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T02:24:12.476925Z","bundle_sha256":"bc6dfdafcee4ce9d144a8dfb59be2db6161eb533307a07f4810a8d19a34163c7"}}