{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:YIGYMOWHEKT234TQELXBM5PE7E","short_pith_number":"pith:YIGYMOWH","canonical_record":{"source":{"id":"2310.10953","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-10-17T02:58:49Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"236b506f16b64ae738cef40bf91a5a782a021d89bef1b2e88477f3da8353a5ec","abstract_canon_sha256":"66e24c8ab4ef9f686eb22f1a5cb9262cb83bb587da829cb334150462a010443c"},"schema_version":"1.0"},"canonical_sha256":"c20d863ac722a7adf27022ee1675e4f9009be1f7d9a6f605f6ba06619db93d02","source":{"kind":"arxiv","id":"2310.10953","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.10953","created_at":"2026-07-05T07:01:29Z"},{"alias_kind":"arxiv_version","alias_value":"2310.10953v1","created_at":"2026-07-05T07:01:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.10953","created_at":"2026-07-05T07:01:29Z"},{"alias_kind":"pith_short_12","alias_value":"YIGYMOWHEKT2","created_at":"2026-07-05T07:01:29Z"},{"alias_kind":"pith_short_16","alias_value":"YIGYMOWHEKT234TQ","created_at":"2026-07-05T07:01:29Z"},{"alias_kind":"pith_short_8","alias_value":"YIGYMOWH","created_at":"2026-07-05T07:01:29Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:YIGYMOWHEKT234TQELXBM5PE7E","target":"record","payload":{"canonical_record":{"source":{"id":"2310.10953","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-10-17T02:58:49Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"236b506f16b64ae738cef40bf91a5a782a021d89bef1b2e88477f3da8353a5ec","abstract_canon_sha256":"66e24c8ab4ef9f686eb22f1a5cb9262cb83bb587da829cb334150462a010443c"},"schema_version":"1.0"},"canonical_sha256":"c20d863ac722a7adf27022ee1675e4f9009be1f7d9a6f605f6ba06619db93d02","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:01:29.956237Z","signature_b64":"QGXdUO4k9saaevgQNc5VlPjGfpd02eQeV7u/xK5AgeWrnMVppdSBKSQCaY6kFX965wgQ5+G7MxHO7kC1isxtBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c20d863ac722a7adf27022ee1675e4f9009be1f7d9a6f605f6ba06619db93d02","last_reissued_at":"2026-07-05T07:01:29.955774Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:01:29.955774Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2310.10953","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":"2026-07-05T07:01:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DwtQ3XKILxdjxECPlnXK0gB4EGjOKoGGi9HP4HcFGtCT3/792Lye9lMBTflu9++FFBc6JZ2IuFFgvFVUGlP0Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T19:39:14.209131Z"},"content_sha256":"669d0df7a7cd4a3e75aced6392b4e6caa798a64c0f4f8074f2128263ccaa86e9","schema_version":"1.0","event_id":"sha256:669d0df7a7cd4a3e75aced6392b4e6caa798a64c0f4f8074f2128263ccaa86e9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:YIGYMOWHEKT234TQELXBM5PE7E","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Local Graph Limits Perspective on Sampling-Based GNNs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Amin Saberi, Luana Ruiz, Yeganeh Alimohammadi","submitted_at":"2023-10-17T02:58:49Z","abstract_excerpt":"We propose a theoretical framework for training Graph Neural Networks (GNNs) on large input graphs via training on small, fixed-size sampled subgraphs. This framework is applicable to a wide range of models, including popular sampling-based GNNs, such as GraphSAGE and FastGCN. Leveraging the theory of graph local limits, we prove that, under mild assumptions, parameters learned from training sampling-based GNNs on small samples of a large input graph are within an $\\epsilon$-neighborhood of the outcome of training the same architecture on the whole graph. We derive bounds on the number of samp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.10953","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/2310.10953/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":"2026-07-05T07:01:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Qnlc90nfrzg+VdmPF7slhj62hv95tzBpt+DTWq5+WhKJZY+1RtvgcGgQxJrljkKMdIFyhYDNKC2mrd7Ov1DNAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T19:39:14.209663Z"},"content_sha256":"a12eea544e9d2baf0da422546992741154bcad0a8e4222115af3bbf53dca1c12","schema_version":"1.0","event_id":"sha256:a12eea544e9d2baf0da422546992741154bcad0a8e4222115af3bbf53dca1c12"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YIGYMOWHEKT234TQELXBM5PE7E/bundle.json","state_url":"https://pith.science/pith/YIGYMOWHEKT234TQELXBM5PE7E/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YIGYMOWHEKT234TQELXBM5PE7E/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":"2026-08-11T19:39:14Z","links":{"resolver":"https://pith.science/pith/YIGYMOWHEKT234TQELXBM5PE7E","bundle":"https://pith.science/pith/YIGYMOWHEKT234TQELXBM5PE7E/bundle.json","state":"https://pith.science/pith/YIGYMOWHEKT234TQELXBM5PE7E/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YIGYMOWHEKT234TQELXBM5PE7E/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:YIGYMOWHEKT234TQELXBM5PE7E","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":"66e24c8ab4ef9f686eb22f1a5cb9262cb83bb587da829cb334150462a010443c","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-10-17T02:58:49Z","title_canon_sha256":"236b506f16b64ae738cef40bf91a5a782a021d89bef1b2e88477f3da8353a5ec"},"schema_version":"1.0","source":{"id":"2310.10953","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.10953","created_at":"2026-07-05T07:01:29Z"},{"alias_kind":"arxiv_version","alias_value":"2310.10953v1","created_at":"2026-07-05T07:01:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.10953","created_at":"2026-07-05T07:01:29Z"},{"alias_kind":"pith_short_12","alias_value":"YIGYMOWHEKT2","created_at":"2026-07-05T07:01:29Z"},{"alias_kind":"pith_short_16","alias_value":"YIGYMOWHEKT234TQ","created_at":"2026-07-05T07:01:29Z"},{"alias_kind":"pith_short_8","alias_value":"YIGYMOWH","created_at":"2026-07-05T07:01:29Z"}],"graph_snapshots":[{"event_id":"sha256:a12eea544e9d2baf0da422546992741154bcad0a8e4222115af3bbf53dca1c12","target":"graph","created_at":"2026-07-05T07:01:29Z","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/2310.10953/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We propose a theoretical framework for training Graph Neural Networks (GNNs) on large input graphs via training on small, fixed-size sampled subgraphs. This framework is applicable to a wide range of models, including popular sampling-based GNNs, such as GraphSAGE and FastGCN. Leveraging the theory of graph local limits, we prove that, under mild assumptions, parameters learned from training sampling-based GNNs on small samples of a large input graph are within an $\\epsilon$-neighborhood of the outcome of training the same architecture on the whole graph. We derive bounds on the number of samp","authors_text":"Amin Saberi, Luana Ruiz, Yeganeh Alimohammadi","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-10-17T02:58:49Z","title":"A Local Graph Limits Perspective on Sampling-Based GNNs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.10953","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":"sha256:669d0df7a7cd4a3e75aced6392b4e6caa798a64c0f4f8074f2128263ccaa86e9","target":"record","created_at":"2026-07-05T07:01:29Z","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":"66e24c8ab4ef9f686eb22f1a5cb9262cb83bb587da829cb334150462a010443c","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-10-17T02:58:49Z","title_canon_sha256":"236b506f16b64ae738cef40bf91a5a782a021d89bef1b2e88477f3da8353a5ec"},"schema_version":"1.0","source":{"id":"2310.10953","kind":"arxiv","version":1}},"canonical_sha256":"c20d863ac722a7adf27022ee1675e4f9009be1f7d9a6f605f6ba06619db93d02","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c20d863ac722a7adf27022ee1675e4f9009be1f7d9a6f605f6ba06619db93d02","first_computed_at":"2026-07-05T07:01:29.955774Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:01:29.955774Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"QGXdUO4k9saaevgQNc5VlPjGfpd02eQeV7u/xK5AgeWrnMVppdSBKSQCaY6kFX965wgQ5+G7MxHO7kC1isxtBw==","signature_status":"signed_v1","signed_at":"2026-07-05T07:01:29.956237Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.10953","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:669d0df7a7cd4a3e75aced6392b4e6caa798a64c0f4f8074f2128263ccaa86e9","sha256:a12eea544e9d2baf0da422546992741154bcad0a8e4222115af3bbf53dca1c12"],"state_sha256":"190cc646460b2890215db481d8aabf0b4524d22bbcc10c27d3d08a7b505b7a22"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7DgQRHozI/qzj68YVG+zriMQ39ZNn6uDHD2BIbm4YwddFtZHRRYAvB3VsM5RyV2Shtk2MveGS6ni1FEjW460Cw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T19:39:14.213872Z","bundle_sha256":"3c2738da6f881854cd67368d4befbe676683e92bed3a4ca12c967a6bae88f3c9"}}