{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:3IA7WSDXCXYLHIVCQ3WOWRJLLO","short_pith_number":"pith:3IA7WSDX","canonical_record":{"source":{"id":"2406.02953","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-05T05:22:32Z","cross_cats_sorted":[],"title_canon_sha256":"e27f8d0358c1d946d5e75f141b72202b04912db068b7cb8a3af93d6433649ce0","abstract_canon_sha256":"5956a91b858a9bb806b2456683890d41109f3a176fc738f0809ae0c98a582c99"},"schema_version":"1.0"},"canonical_sha256":"da01fb487715f0b3a2a286eceb452b5b8a17205094b2bb7b61ab69f0c1d20818","source":{"kind":"arxiv","id":"2406.02953","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.02953","created_at":"2026-07-05T08:27:33Z"},{"alias_kind":"arxiv_version","alias_value":"2406.02953v1","created_at":"2026-07-05T08:27:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.02953","created_at":"2026-07-05T08:27:33Z"},{"alias_kind":"pith_short_12","alias_value":"3IA7WSDXCXYL","created_at":"2026-07-05T08:27:33Z"},{"alias_kind":"pith_short_16","alias_value":"3IA7WSDXCXYLHIVC","created_at":"2026-07-05T08:27:33Z"},{"alias_kind":"pith_short_8","alias_value":"3IA7WSDX","created_at":"2026-07-05T08:27:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:3IA7WSDXCXYLHIVCQ3WOWRJLLO","target":"record","payload":{"canonical_record":{"source":{"id":"2406.02953","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-05T05:22:32Z","cross_cats_sorted":[],"title_canon_sha256":"e27f8d0358c1d946d5e75f141b72202b04912db068b7cb8a3af93d6433649ce0","abstract_canon_sha256":"5956a91b858a9bb806b2456683890d41109f3a176fc738f0809ae0c98a582c99"},"schema_version":"1.0"},"canonical_sha256":"da01fb487715f0b3a2a286eceb452b5b8a17205094b2bb7b61ab69f0c1d20818","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:27:33.266885Z","signature_b64":"ReIp0Hr7V9xl87zjbcJ0SPMUuxMSRRdgng4MhV1JAoRIdZDexkilMrelBWw/65ruAwQRy3eGLUDGcspwHZ1zBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"da01fb487715f0b3a2a286eceb452b5b8a17205094b2bb7b61ab69f0c1d20818","last_reissued_at":"2026-07-05T08:27:33.266327Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:27:33.266327Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2406.02953","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":"2026-07-05T08:27:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ekf+HxmrZDW+ZjIsi9IQilq5b6PPZPXEbWTU/h8UJoFEz3aJ+AxqcnmDzGcAYm1PI5V1i2u7onT9hHebKL4HCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T19:35:38.652465Z"},"content_sha256":"bd671fd3272b75970a2bde5e78262d7ba4b518bb0ec82349e52bfc88829e6442","schema_version":"1.0","event_id":"sha256:bd671fd3272b75970a2bde5e78262d7ba4b518bb0ec82349e52bfc88829e6442"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:3IA7WSDXCXYLHIVCQ3WOWRJLLO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"GraphAlign: Pretraining One Graph Neural Network on Multiple Graphs via Feature Alignment","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Haozhan Li, Jie Tang, Yukuo Cen, Yuxiao Dong, Zhenyu Hou","submitted_at":"2024-06-05T05:22:32Z","abstract_excerpt":"Graph self-supervised learning (SSL) holds considerable promise for mining and learning with graph-structured data. Yet, a significant challenge in graph SSL lies in the feature discrepancy among graphs across different domains. In this work, we aim to pretrain one graph neural network (GNN) on a varied collection of graphs endowed with rich node features and subsequently apply the pretrained GNN to unseen graphs. We present a general GraphAlign method that can be seamlessly integrated into the existing graph SSL framework. To align feature distributions across disparate graphs, GraphAlign des"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.02953","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/2406.02953/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":"2026-07-05T08:27:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9JFZWcOqx1noyIBqs/4PkyVBEgYzYG4r+jX3uXAx9NRIPp6J/bHO06KmI5B+0DXkxRATOePQA0UTzA+MgjWuBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T19:35:38.652873Z"},"content_sha256":"877a42fc94866260adddbdede15492dcd9bfd90c2f4fe5c53b8a6980f2d85c99","schema_version":"1.0","event_id":"sha256:877a42fc94866260adddbdede15492dcd9bfd90c2f4fe5c53b8a6980f2d85c99"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3IA7WSDXCXYLHIVCQ3WOWRJLLO/bundle.json","state_url":"https://pith.science/pith/3IA7WSDXCXYLHIVCQ3WOWRJLLO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3IA7WSDXCXYLHIVCQ3WOWRJLLO/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":"2026-07-06T19:35:38Z","links":{"resolver":"https://pith.science/pith/3IA7WSDXCXYLHIVCQ3WOWRJLLO","bundle":"https://pith.science/pith/3IA7WSDXCXYLHIVCQ3WOWRJLLO/bundle.json","state":"https://pith.science/pith/3IA7WSDXCXYLHIVCQ3WOWRJLLO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3IA7WSDXCXYLHIVCQ3WOWRJLLO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:3IA7WSDXCXYLHIVCQ3WOWRJLLO","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":"5956a91b858a9bb806b2456683890d41109f3a176fc738f0809ae0c98a582c99","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-05T05:22:32Z","title_canon_sha256":"e27f8d0358c1d946d5e75f141b72202b04912db068b7cb8a3af93d6433649ce0"},"schema_version":"1.0","source":{"id":"2406.02953","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.02953","created_at":"2026-07-05T08:27:33Z"},{"alias_kind":"arxiv_version","alias_value":"2406.02953v1","created_at":"2026-07-05T08:27:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.02953","created_at":"2026-07-05T08:27:33Z"},{"alias_kind":"pith_short_12","alias_value":"3IA7WSDXCXYL","created_at":"2026-07-05T08:27:33Z"},{"alias_kind":"pith_short_16","alias_value":"3IA7WSDXCXYLHIVC","created_at":"2026-07-05T08:27:33Z"},{"alias_kind":"pith_short_8","alias_value":"3IA7WSDX","created_at":"2026-07-05T08:27:33Z"}],"graph_snapshots":[{"event_id":"sha256:877a42fc94866260adddbdede15492dcd9bfd90c2f4fe5c53b8a6980f2d85c99","target":"graph","created_at":"2026-07-05T08:27:33Z","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/2406.02953/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graph self-supervised learning (SSL) holds considerable promise for mining and learning with graph-structured data. Yet, a significant challenge in graph SSL lies in the feature discrepancy among graphs across different domains. In this work, we aim to pretrain one graph neural network (GNN) on a varied collection of graphs endowed with rich node features and subsequently apply the pretrained GNN to unseen graphs. We present a general GraphAlign method that can be seamlessly integrated into the existing graph SSL framework. To align feature distributions across disparate graphs, GraphAlign des","authors_text":"Haozhan Li, Jie Tang, Yukuo Cen, Yuxiao Dong, Zhenyu Hou","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-05T05:22:32Z","title":"GraphAlign: Pretraining One Graph Neural Network on Multiple Graphs via Feature Alignment"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.02953","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:bd671fd3272b75970a2bde5e78262d7ba4b518bb0ec82349e52bfc88829e6442","target":"record","created_at":"2026-07-05T08:27:33Z","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":"5956a91b858a9bb806b2456683890d41109f3a176fc738f0809ae0c98a582c99","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-05T05:22:32Z","title_canon_sha256":"e27f8d0358c1d946d5e75f141b72202b04912db068b7cb8a3af93d6433649ce0"},"schema_version":"1.0","source":{"id":"2406.02953","kind":"arxiv","version":1}},"canonical_sha256":"da01fb487715f0b3a2a286eceb452b5b8a17205094b2bb7b61ab69f0c1d20818","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"da01fb487715f0b3a2a286eceb452b5b8a17205094b2bb7b61ab69f0c1d20818","first_computed_at":"2026-07-05T08:27:33.266327Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:27:33.266327Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ReIp0Hr7V9xl87zjbcJ0SPMUuxMSRRdgng4MhV1JAoRIdZDexkilMrelBWw/65ruAwQRy3eGLUDGcspwHZ1zBg==","signature_status":"signed_v1","signed_at":"2026-07-05T08:27:33.266885Z","signed_message":"canonical_sha256_bytes"},"source_id":"2406.02953","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bd671fd3272b75970a2bde5e78262d7ba4b518bb0ec82349e52bfc88829e6442","sha256:877a42fc94866260adddbdede15492dcd9bfd90c2f4fe5c53b8a6980f2d85c99"],"state_sha256":"98656040a9e43dfff0f93895f2469a9317766c03e9952f58eef2f2e6455d7804"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EinZOEveKA48AbXo0axsZe++CUMKQJQd4b1IVXVND/rQ0xWd4TOIANmQskzY1ZWH+iLG3fg09mC/JamyJlBiBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T19:35:38.654900Z","bundle_sha256":"b8546c9403811273bc96cee4f3fcea7dc9d330d4f531ffb510ea160b9ac77d86"}}