{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:AHID4EFIV5GH3WP6YMRIIGVH6W","short_pith_number":"pith:AHID4EFI","canonical_record":{"source":{"id":"2103.05045","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-03-08T20:01:59Z","cross_cats_sorted":[],"title_canon_sha256":"d3d668c7816afda585402dd7dc4287266054134a3f9654566dfa0458f495d1ad","abstract_canon_sha256":"0bb02ef556b14551276380121789dea6f6bff106fb04f2adfb81bd2187998819"},"schema_version":"1.0"},"canonical_sha256":"01d03e10a8af4c7dd9fec322841aa7f596568841011e4a4cdf4d73576b75a4eb","source":{"kind":"arxiv","id":"2103.05045","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2103.05045","created_at":"2026-07-05T03:38:16Z"},{"alias_kind":"arxiv_version","alias_value":"2103.05045v2","created_at":"2026-07-05T03:38:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.05045","created_at":"2026-07-05T03:38:16Z"},{"alias_kind":"pith_short_12","alias_value":"AHID4EFIV5GH","created_at":"2026-07-05T03:38:16Z"},{"alias_kind":"pith_short_16","alias_value":"AHID4EFIV5GH3WP6","created_at":"2026-07-05T03:38:16Z"},{"alias_kind":"pith_short_8","alias_value":"AHID4EFI","created_at":"2026-07-05T03:38:16Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:AHID4EFIV5GH3WP6YMRIIGVH6W","target":"record","payload":{"canonical_record":{"source":{"id":"2103.05045","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-03-08T20:01:59Z","cross_cats_sorted":[],"title_canon_sha256":"d3d668c7816afda585402dd7dc4287266054134a3f9654566dfa0458f495d1ad","abstract_canon_sha256":"0bb02ef556b14551276380121789dea6f6bff106fb04f2adfb81bd2187998819"},"schema_version":"1.0"},"canonical_sha256":"01d03e10a8af4c7dd9fec322841aa7f596568841011e4a4cdf4d73576b75a4eb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:38:16.299160Z","signature_b64":"PtfTAnnCsYx8YRn6O+XFzKKm9xef7yJGDW0B0pvwbnRqqIcfgt7KR5MhZT0ZEMDAoCDko/y7yErbxe9W+2dJDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"01d03e10a8af4c7dd9fec322841aa7f596568841011e4a4cdf4d73576b75a4eb","last_reissued_at":"2026-07-05T03:38:16.298697Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:38:16.298697Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2103.05045","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:38:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9K30tI7IYc+ug5sZvvWguqlRG6fQCEgV7gURuG3I4IiULkZ9hx2hqnURUqinuqX8X2etN23Y7BVpTXP7KGKJCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T14:27:06.209911Z"},"content_sha256":"9fb52f1ff341175da50fc2a4820bfa2cd6c9298c4681f118b6b96330f6d1eff9","schema_version":"1.0","event_id":"sha256:9fb52f1ff341175da50fc2a4820bfa2cd6c9298c4681f118b6b96330f6d1eff9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:AHID4EFIV5GH3WP6YMRIIGVH6W","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Size-Invariant Graph Representations for Graph Classification Extrapolations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Beatrice Bevilacqua, Bruno Ribeiro, Yangze Zhou","submitted_at":"2021-03-08T20:01:59Z","abstract_excerpt":"In general, graph representation learning methods assume that the train and test data come from the same distribution. In this work we consider an underexplored area of an otherwise rapidly developing field of graph representation learning: The task of out-of-distribution (OOD) graph classification, where train and test data have different distributions, with test data unavailable during training. Our work shows it is possible to use a causal model to learn approximately invariant representations that better extrapolate between train and test data. Finally, we conclude with synthetic and real-"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.05045","kind":"arxiv","version":2},"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/2103.05045/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":"2026-07-05T03:38:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MNETYvtdIVcT/HOdJhz6KU77r3bZAyDCp5N/uLqxr1FtU9K/jwqropqUCq5Qsw2lkaopWUo72ETP1JEVO+jqDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T14:27:06.210394Z"},"content_sha256":"35edbd7ded5d73e7b16ce42edc35c24629a676e2c6297a8d874166b0f4622c1f","schema_version":"1.0","event_id":"sha256:35edbd7ded5d73e7b16ce42edc35c24629a676e2c6297a8d874166b0f4622c1f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AHID4EFIV5GH3WP6YMRIIGVH6W/bundle.json","state_url":"https://pith.science/pith/AHID4EFIV5GH3WP6YMRIIGVH6W/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AHID4EFIV5GH3WP6YMRIIGVH6W/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":"2026-08-05T14:27:06Z","links":{"resolver":"https://pith.science/pith/AHID4EFIV5GH3WP6YMRIIGVH6W","bundle":"https://pith.science/pith/AHID4EFIV5GH3WP6YMRIIGVH6W/bundle.json","state":"https://pith.science/pith/AHID4EFIV5GH3WP6YMRIIGVH6W/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AHID4EFIV5GH3WP6YMRIIGVH6W/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:AHID4EFIV5GH3WP6YMRIIGVH6W","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":"0bb02ef556b14551276380121789dea6f6bff106fb04f2adfb81bd2187998819","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-03-08T20:01:59Z","title_canon_sha256":"d3d668c7816afda585402dd7dc4287266054134a3f9654566dfa0458f495d1ad"},"schema_version":"1.0","source":{"id":"2103.05045","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2103.05045","created_at":"2026-07-05T03:38:16Z"},{"alias_kind":"arxiv_version","alias_value":"2103.05045v2","created_at":"2026-07-05T03:38:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.05045","created_at":"2026-07-05T03:38:16Z"},{"alias_kind":"pith_short_12","alias_value":"AHID4EFIV5GH","created_at":"2026-07-05T03:38:16Z"},{"alias_kind":"pith_short_16","alias_value":"AHID4EFIV5GH3WP6","created_at":"2026-07-05T03:38:16Z"},{"alias_kind":"pith_short_8","alias_value":"AHID4EFI","created_at":"2026-07-05T03:38:16Z"}],"graph_snapshots":[{"event_id":"sha256:35edbd7ded5d73e7b16ce42edc35c24629a676e2c6297a8d874166b0f4622c1f","target":"graph","created_at":"2026-07-05T03:38:16Z","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/2103.05045/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In general, graph representation learning methods assume that the train and test data come from the same distribution. In this work we consider an underexplored area of an otherwise rapidly developing field of graph representation learning: The task of out-of-distribution (OOD) graph classification, where train and test data have different distributions, with test data unavailable during training. Our work shows it is possible to use a causal model to learn approximately invariant representations that better extrapolate between train and test data. Finally, we conclude with synthetic and real-","authors_text":"Beatrice Bevilacqua, Bruno Ribeiro, Yangze Zhou","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-03-08T20:01:59Z","title":"Size-Invariant Graph Representations for Graph Classification Extrapolations"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.05045","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":"sha256:9fb52f1ff341175da50fc2a4820bfa2cd6c9298c4681f118b6b96330f6d1eff9","target":"record","created_at":"2026-07-05T03:38:16Z","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":"0bb02ef556b14551276380121789dea6f6bff106fb04f2adfb81bd2187998819","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-03-08T20:01:59Z","title_canon_sha256":"d3d668c7816afda585402dd7dc4287266054134a3f9654566dfa0458f495d1ad"},"schema_version":"1.0","source":{"id":"2103.05045","kind":"arxiv","version":2}},"canonical_sha256":"01d03e10a8af4c7dd9fec322841aa7f596568841011e4a4cdf4d73576b75a4eb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"01d03e10a8af4c7dd9fec322841aa7f596568841011e4a4cdf4d73576b75a4eb","first_computed_at":"2026-07-05T03:38:16.298697Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:38:16.298697Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PtfTAnnCsYx8YRn6O+XFzKKm9xef7yJGDW0B0pvwbnRqqIcfgt7KR5MhZT0ZEMDAoCDko/y7yErbxe9W+2dJDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T03:38:16.299160Z","signed_message":"canonical_sha256_bytes"},"source_id":"2103.05045","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9fb52f1ff341175da50fc2a4820bfa2cd6c9298c4681f118b6b96330f6d1eff9","sha256:35edbd7ded5d73e7b16ce42edc35c24629a676e2c6297a8d874166b0f4622c1f"],"state_sha256":"e42bfa699a79add0fbb3879e2ed34b727ca267e38ae71f7ad9de1ffbca226da6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"n96jy7UTI0sHtf6N6BKnLuM5dcikZVT5XQFrSQOjumsDuOUnSV3MfzMFg1lOUZ9JEViJU9eUlcK/+5J9o3wXCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T14:27:06.214159Z","bundle_sha256":"43dc1d2acb30292bdd33c7def21ec066377e7a45de54527338a9d4ad2fa65d84"}}