{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:UIQCBVW6BH7JMYWN2L32HGL2XR","short_pith_number":"pith:UIQCBVW6","schema_version":"1.0","canonical_sha256":"a22020d6de09fe9662cdd2f7a3997abc5d603d8758c7d3ced9fe60cf28b2c70c","source":{"kind":"arxiv","id":"2507.16991","version":2},"attestation_state":"computed","paper":{"title":"PyG 2.0: Scalable Learning on Real World Graphs","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Akihiro Nitta, Alexandria Barghi, Bla\\v{z} Stojanovi\\v{c}, Jan Eric Lenssen, Jinu Sunil, Jure Leskovec, Manan Shah, Matthias Fey, Ramona Bendias, Rishi Puri, Vid Kocijan, Xinwei He, Zecheng Zhang","submitted_at":"2025-07-22T19:55:09Z","abstract_excerpt":"PyG (PyTorch Geometric) has evolved significantly since its initial release, establishing itself as a leading framework for Graph Neural Networks. In this paper, we present Pyg 2.0 (and its subsequent minor versions), a comprehensive update that introduces substantial improvements in scalability and real-world application capabilities. We detail the framework's enhanced architecture, including support for heterogeneous and temporal graphs, scalable feature/graph stores, and various optimizations, enabling researchers and practitioners to tackle large-scale graph learning problems efficiently. "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2507.16991","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-22T19:55:09Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"dce68723a37214bb4f0ae4a9f60386b18218c56e8f79240b02a563625927ae61","abstract_canon_sha256":"30c0f6c56e6a786b2848efb0a5640a454d094072a98a61fb3f6bce3dcfe904a7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:44:10.035962Z","signature_b64":"7FL99CzuIUda9iAISjJmBP9FZR0csgG7oefRZ1bjk6DHyZTqbe8/KroRPnbSljs5VagfThdJtkojKMmTtIe5AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a22020d6de09fe9662cdd2f7a3997abc5d603d8758c7d3ced9fe60cf28b2c70c","last_reissued_at":"2026-07-05T11:44:10.035404Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:44:10.035404Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PyG 2.0: Scalable Learning on Real World Graphs","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Akihiro Nitta, Alexandria Barghi, Bla\\v{z} Stojanovi\\v{c}, Jan Eric Lenssen, Jinu Sunil, Jure Leskovec, Manan Shah, Matthias Fey, Ramona Bendias, Rishi Puri, Vid Kocijan, Xinwei He, Zecheng Zhang","submitted_at":"2025-07-22T19:55:09Z","abstract_excerpt":"PyG (PyTorch Geometric) has evolved significantly since its initial release, establishing itself as a leading framework for Graph Neural Networks. In this paper, we present Pyg 2.0 (and its subsequent minor versions), a comprehensive update that introduces substantial improvements in scalability and real-world application capabilities. We detail the framework's enhanced architecture, including support for heterogeneous and temporal graphs, scalable feature/graph stores, and various optimizations, enabling researchers and practitioners to tackle large-scale graph learning problems efficiently. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.16991","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/2507.16991/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2507.16991","created_at":"2026-07-05T11:44:10.035465+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.16991v2","created_at":"2026-07-05T11:44:10.035465+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.16991","created_at":"2026-07-05T11:44:10.035465+00:00"},{"alias_kind":"pith_short_12","alias_value":"UIQCBVW6BH7J","created_at":"2026-07-05T11:44:10.035465+00:00"},{"alias_kind":"pith_short_16","alias_value":"UIQCBVW6BH7JMYWN","created_at":"2026-07-05T11:44:10.035465+00:00"},{"alias_kind":"pith_short_8","alias_value":"UIQCBVW6","created_at":"2026-07-05T11:44:10.035465+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.18379","citing_title":"RankGraph-2: Lifecycle Co-Design for Billion-Node Graph Learning in Recommendation","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2605.31500","citing_title":"On Efficient Scaling of GNNs via IO-Aware Layers Implementations","ref_index":8,"is_internal_anchor":false},{"citing_arxiv_id":"2606.18379","citing_title":"RankGraph-2: Lifecycle Co-Design for Billion-Node Graph Learning in Recommendation","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2605.31315","citing_title":"Graph Neural Networks Are Not Continuous Across Graph Resolutions","ref_index":14,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UIQCBVW6BH7JMYWN2L32HGL2XR","json":"https://pith.science/pith/UIQCBVW6BH7JMYWN2L32HGL2XR.json","graph_json":"https://pith.science/api/pith-number/UIQCBVW6BH7JMYWN2L32HGL2XR/graph.json","events_json":"https://pith.science/api/pith-number/UIQCBVW6BH7JMYWN2L32HGL2XR/events.json","paper":"https://pith.science/paper/UIQCBVW6"},"agent_actions":{"view_html":"https://pith.science/pith/UIQCBVW6BH7JMYWN2L32HGL2XR","download_json":"https://pith.science/pith/UIQCBVW6BH7JMYWN2L32HGL2XR.json","view_paper":"https://pith.science/paper/UIQCBVW6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.16991&json=true","fetch_graph":"https://pith.science/api/pith-number/UIQCBVW6BH7JMYWN2L32HGL2XR/graph.json","fetch_events":"https://pith.science/api/pith-number/UIQCBVW6BH7JMYWN2L32HGL2XR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UIQCBVW6BH7JMYWN2L32HGL2XR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UIQCBVW6BH7JMYWN2L32HGL2XR/action/storage_attestation","attest_author":"https://pith.science/pith/UIQCBVW6BH7JMYWN2L32HGL2XR/action/author_attestation","sign_citation":"https://pith.science/pith/UIQCBVW6BH7JMYWN2L32HGL2XR/action/citation_signature","submit_replication":"https://pith.science/pith/UIQCBVW6BH7JMYWN2L32HGL2XR/action/replication_record"}},"created_at":"2026-07-05T11:44:10.035465+00:00","updated_at":"2026-07-05T11:44:10.035465+00:00"}