{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:CK6OGTRCT47SMYCMQKFEF57YEE","short_pith_number":"pith:CK6OGTRC","canonical_record":{"source":{"id":"2111.06483","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-11-11T22:27:59Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"2bd5c87a7f029bc66ea5b319b71a2fb7ca094e63bd805616328943f1b7db0869","abstract_canon_sha256":"079b3cf0359494db6088a4457a283ab9c036d305eded6d49411814728edc7b4e"},"schema_version":"1.0"},"canonical_sha256":"12bce34e229f3f26604c828a42f7f82111252820526de48b03801e285db34cbc","source":{"kind":"arxiv","id":"2111.06483","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2111.06483","created_at":"2026-07-05T04:14:59Z"},{"alias_kind":"arxiv_version","alias_value":"2111.06483v3","created_at":"2026-07-05T04:14:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.06483","created_at":"2026-07-05T04:14:59Z"},{"alias_kind":"pith_short_12","alias_value":"CK6OGTRCT47S","created_at":"2026-07-05T04:14:59Z"},{"alias_kind":"pith_short_16","alias_value":"CK6OGTRCT47SMYCM","created_at":"2026-07-05T04:14:59Z"},{"alias_kind":"pith_short_8","alias_value":"CK6OGTRC","created_at":"2026-07-05T04:14:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:CK6OGTRCT47SMYCMQKFEF57YEE","target":"record","payload":{"canonical_record":{"source":{"id":"2111.06483","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-11-11T22:27:59Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"2bd5c87a7f029bc66ea5b319b71a2fb7ca094e63bd805616328943f1b7db0869","abstract_canon_sha256":"079b3cf0359494db6088a4457a283ab9c036d305eded6d49411814728edc7b4e"},"schema_version":"1.0"},"canonical_sha256":"12bce34e229f3f26604c828a42f7f82111252820526de48b03801e285db34cbc","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:14:59.963023Z","signature_b64":"VNgiIxxrrJ0tuz3rB3zvTkwqIIEQPThuGz0NJ/ag2kx471DzJH+iagtAuDuC5bQC9U0Ni271a1at+ZCuMjRiAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"12bce34e229f3f26604c828a42f7f82111252820526de48b03801e285db34cbc","last_reissued_at":"2026-07-05T04:14:59.962526Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:14:59.962526Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2111.06483","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:14:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0maJq8XQTe70Znw4NOXt5xa1R8CZruOtYGZIdncH/PDXAtlv+fXL39hZZzU2ONoyhB5oc+CVPdNahSbt/CYXAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T22:23:54.896448Z"},"content_sha256":"02a177b261766a69f7d103da0199d32b5dd150351728d201dc6a0f5ea6683b9f","schema_version":"1.0","event_id":"sha256:02a177b261766a69f7d103da0199d32b5dd150351728d201dc6a0f5ea6683b9f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:CK6OGTRCT47SMYCMQKFEF57YEE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Sequential Aggregation and Rematerialization: Distributed Full-batch Training of Graph Neural Networks on Large Graphs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Hesham Mostafa","submitted_at":"2021-11-11T22:27:59Z","abstract_excerpt":"We present the Sequential Aggregation and Rematerialization (SAR) scheme for distributed full-batch training of Graph Neural Networks (GNNs) on large graphs. Large-scale training of GNNs has recently been dominated by sampling-based methods and methods based on non-learnable message passing. SAR on the other hand is a distributed technique that can train any GNN type directly on an entire large graph. The key innovation in SAR is the distributed sequential rematerialization scheme which sequentially re-constructs then frees pieces of the prohibitively large GNN computational graph during the b"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.06483","kind":"arxiv","version":3},"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/2111.06483/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":"2026-07-05T04:14:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NcsGc5ydD18eZa9ABLFMw+AgV2Z6UhNYr45QRR8y4kBgbmd3+mP1zdob5/bqM/u5H+l9xw9YbA6eX4EAeicMCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T22:23:54.896979Z"},"content_sha256":"5cdd6884758324645ce0f7119f0182a42c04956391d8cce10b0b1df3f0386156","schema_version":"1.0","event_id":"sha256:5cdd6884758324645ce0f7119f0182a42c04956391d8cce10b0b1df3f0386156"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CK6OGTRCT47SMYCMQKFEF57YEE/bundle.json","state_url":"https://pith.science/pith/CK6OGTRCT47SMYCMQKFEF57YEE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CK6OGTRCT47SMYCMQKFEF57YEE/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":"2026-08-08T22:23:54Z","links":{"resolver":"https://pith.science/pith/CK6OGTRCT47SMYCMQKFEF57YEE","bundle":"https://pith.science/pith/CK6OGTRCT47SMYCMQKFEF57YEE/bundle.json","state":"https://pith.science/pith/CK6OGTRCT47SMYCMQKFEF57YEE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CK6OGTRCT47SMYCMQKFEF57YEE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:CK6OGTRCT47SMYCMQKFEF57YEE","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":"079b3cf0359494db6088a4457a283ab9c036d305eded6d49411814728edc7b4e","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-11-11T22:27:59Z","title_canon_sha256":"2bd5c87a7f029bc66ea5b319b71a2fb7ca094e63bd805616328943f1b7db0869"},"schema_version":"1.0","source":{"id":"2111.06483","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2111.06483","created_at":"2026-07-05T04:14:59Z"},{"alias_kind":"arxiv_version","alias_value":"2111.06483v3","created_at":"2026-07-05T04:14:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.06483","created_at":"2026-07-05T04:14:59Z"},{"alias_kind":"pith_short_12","alias_value":"CK6OGTRCT47S","created_at":"2026-07-05T04:14:59Z"},{"alias_kind":"pith_short_16","alias_value":"CK6OGTRCT47SMYCM","created_at":"2026-07-05T04:14:59Z"},{"alias_kind":"pith_short_8","alias_value":"CK6OGTRC","created_at":"2026-07-05T04:14:59Z"}],"graph_snapshots":[{"event_id":"sha256:5cdd6884758324645ce0f7119f0182a42c04956391d8cce10b0b1df3f0386156","target":"graph","created_at":"2026-07-05T04:14:59Z","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/2111.06483/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We present the Sequential Aggregation and Rematerialization (SAR) scheme for distributed full-batch training of Graph Neural Networks (GNNs) on large graphs. Large-scale training of GNNs has recently been dominated by sampling-based methods and methods based on non-learnable message passing. SAR on the other hand is a distributed technique that can train any GNN type directly on an entire large graph. The key innovation in SAR is the distributed sequential rematerialization scheme which sequentially re-constructs then frees pieces of the prohibitively large GNN computational graph during the b","authors_text":"Hesham Mostafa","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-11-11T22:27:59Z","title":"Sequential Aggregation and Rematerialization: Distributed Full-batch Training of Graph Neural Networks on Large Graphs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.06483","kind":"arxiv","version":3},"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":"sha256:02a177b261766a69f7d103da0199d32b5dd150351728d201dc6a0f5ea6683b9f","target":"record","created_at":"2026-07-05T04:14:59Z","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":"079b3cf0359494db6088a4457a283ab9c036d305eded6d49411814728edc7b4e","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-11-11T22:27:59Z","title_canon_sha256":"2bd5c87a7f029bc66ea5b319b71a2fb7ca094e63bd805616328943f1b7db0869"},"schema_version":"1.0","source":{"id":"2111.06483","kind":"arxiv","version":3}},"canonical_sha256":"12bce34e229f3f26604c828a42f7f82111252820526de48b03801e285db34cbc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"12bce34e229f3f26604c828a42f7f82111252820526de48b03801e285db34cbc","first_computed_at":"2026-07-05T04:14:59.962526Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:14:59.962526Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"VNgiIxxrrJ0tuz3rB3zvTkwqIIEQPThuGz0NJ/ag2kx471DzJH+iagtAuDuC5bQC9U0Ni271a1at+ZCuMjRiAg==","signature_status":"signed_v1","signed_at":"2026-07-05T04:14:59.963023Z","signed_message":"canonical_sha256_bytes"},"source_id":"2111.06483","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:02a177b261766a69f7d103da0199d32b5dd150351728d201dc6a0f5ea6683b9f","sha256:5cdd6884758324645ce0f7119f0182a42c04956391d8cce10b0b1df3f0386156"],"state_sha256":"8a911b58c3a48ea7ade56921a43a0e099caf230b749b13faf5b00650f277a6a7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"f+w4O4gmWwNVk/La1O+Xt4lJbQP5JFJ26xRJ8sLYJRh6rbUUYpGSaAYfuN+LqHcDbhEoCNM1p+i94GWvsGqxBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T22:23:54.901114Z","bundle_sha256":"f28b0a0552dfd215b525ef9317a883433a7dc1dae7f70761afecbdaf7888915a"}}