{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:KEYYGIBLYJQ64J4KCMY5WYVBNT","short_pith_number":"pith:KEYYGIBL","canonical_record":{"source":{"id":"2411.02003","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-04T11:42:25Z","cross_cats_sorted":["cs.AI","cs.DC","cs.SI"],"title_canon_sha256":"3e09e3b34a9ac13847915ab2f6e1fa203084fbfd5518385ca7f09f2654d11530","abstract_canon_sha256":"ef4b28da0de8776bfd9072756ec9784539b162abb39a51d24b1faedb7df62ad3"},"schema_version":"1.0"},"canonical_sha256":"513183202bc261ee278a1331db62a16cddaf23b534f4ac4ae64dac9792e5c4f6","source":{"kind":"arxiv","id":"2411.02003","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.02003","created_at":"2026-07-05T09:30:40Z"},{"alias_kind":"arxiv_version","alias_value":"2411.02003v1","created_at":"2026-07-05T09:30:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.02003","created_at":"2026-07-05T09:30:40Z"},{"alias_kind":"pith_short_12","alias_value":"KEYYGIBLYJQ6","created_at":"2026-07-05T09:30:40Z"},{"alias_kind":"pith_short_16","alias_value":"KEYYGIBLYJQ64J4K","created_at":"2026-07-05T09:30:40Z"},{"alias_kind":"pith_short_8","alias_value":"KEYYGIBL","created_at":"2026-07-05T09:30:40Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:KEYYGIBLYJQ64J4KCMY5WYVBNT","target":"record","payload":{"canonical_record":{"source":{"id":"2411.02003","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-04T11:42:25Z","cross_cats_sorted":["cs.AI","cs.DC","cs.SI"],"title_canon_sha256":"3e09e3b34a9ac13847915ab2f6e1fa203084fbfd5518385ca7f09f2654d11530","abstract_canon_sha256":"ef4b28da0de8776bfd9072756ec9784539b162abb39a51d24b1faedb7df62ad3"},"schema_version":"1.0"},"canonical_sha256":"513183202bc261ee278a1331db62a16cddaf23b534f4ac4ae64dac9792e5c4f6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:30:40.040543Z","signature_b64":"buT8Xppr6yJkbLUkCTfS+SK9n47k3M01mmPKAFs6+Yvi4QdltpQZe+uIUgWPxOrUqTosEUii4cHlKVjBzNmUCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"513183202bc261ee278a1331db62a16cddaf23b534f4ac4ae64dac9792e5c4f6","last_reissued_at":"2026-07-05T09:30:40.040060Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:30:40.040060Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2411.02003","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":"2026-07-05T09:30:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vU4pOYdFUKNQS1JxEDHatShPcBf1HWajB4Kl0FKZawuT1bHAxU7YsdDVR5GsX2nnCHQNgzxsq8YvFU1OLphfAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T10:36:22.339235Z"},"content_sha256":"bbb7b6eb37f6a503643cde552cc9d6d9ee4ed3007a50a0e74c5ce75826ac4029","schema_version":"1.0","event_id":"sha256:bbb7b6eb37f6a503643cde552cc9d6d9ee4ed3007a50a0e74c5ce75826ac4029"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:KEYYGIBLYJQ64J4KCMY5WYVBNT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Against Multifaceted Graph Heterogeneity via Asymmetric Federated Prompt Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.DC","cs.SI"],"primary_cat":"cs.LG","authors_text":"Hao Liu, Ruiqian Han, Zhuoning Guo","submitted_at":"2024-11-04T11:42:25Z","abstract_excerpt":"Federated Graph Learning (FGL) aims to collaboratively and privately optimize graph models on divergent data for different tasks. A critical challenge in FGL is to enable effective yet efficient federated optimization against multifaceted graph heterogeneity to enhance mutual performance. However, existing FGL works primarily address graph data heterogeneity and perform incapable of graph task heterogeneity. To address the challenge, we propose a Federated Graph Prompt Learning (FedGPL) framework to efficiently enable prompt-based asymmetric graph knowledge transfer between multifaceted hetero"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.02003","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/2411.02003/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":"2026-07-05T09:30:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/mC/IhB/KdmKBfgQPFFCjWbqgqtBaascglyzXH5GTDNn/egh/E0HbsymJDAN5YeCmB28VIwnun/9Yxh1fFpwCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T10:36:22.339708Z"},"content_sha256":"d2dbe362243bae015508db014b2d7342e2d9bcb73afc566a7f358f1ff00a8b6e","schema_version":"1.0","event_id":"sha256:d2dbe362243bae015508db014b2d7342e2d9bcb73afc566a7f358f1ff00a8b6e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KEYYGIBLYJQ64J4KCMY5WYVBNT/bundle.json","state_url":"https://pith.science/pith/KEYYGIBLYJQ64J4KCMY5WYVBNT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KEYYGIBLYJQ64J4KCMY5WYVBNT/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":"2026-08-07T10:36:22Z","links":{"resolver":"https://pith.science/pith/KEYYGIBLYJQ64J4KCMY5WYVBNT","bundle":"https://pith.science/pith/KEYYGIBLYJQ64J4KCMY5WYVBNT/bundle.json","state":"https://pith.science/pith/KEYYGIBLYJQ64J4KCMY5WYVBNT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KEYYGIBLYJQ64J4KCMY5WYVBNT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:KEYYGIBLYJQ64J4KCMY5WYVBNT","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":"ef4b28da0de8776bfd9072756ec9784539b162abb39a51d24b1faedb7df62ad3","cross_cats_sorted":["cs.AI","cs.DC","cs.SI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-04T11:42:25Z","title_canon_sha256":"3e09e3b34a9ac13847915ab2f6e1fa203084fbfd5518385ca7f09f2654d11530"},"schema_version":"1.0","source":{"id":"2411.02003","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.02003","created_at":"2026-07-05T09:30:40Z"},{"alias_kind":"arxiv_version","alias_value":"2411.02003v1","created_at":"2026-07-05T09:30:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.02003","created_at":"2026-07-05T09:30:40Z"},{"alias_kind":"pith_short_12","alias_value":"KEYYGIBLYJQ6","created_at":"2026-07-05T09:30:40Z"},{"alias_kind":"pith_short_16","alias_value":"KEYYGIBLYJQ64J4K","created_at":"2026-07-05T09:30:40Z"},{"alias_kind":"pith_short_8","alias_value":"KEYYGIBL","created_at":"2026-07-05T09:30:40Z"}],"graph_snapshots":[{"event_id":"sha256:d2dbe362243bae015508db014b2d7342e2d9bcb73afc566a7f358f1ff00a8b6e","target":"graph","created_at":"2026-07-05T09:30:40Z","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/2411.02003/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Federated Graph Learning (FGL) aims to collaboratively and privately optimize graph models on divergent data for different tasks. A critical challenge in FGL is to enable effective yet efficient federated optimization against multifaceted graph heterogeneity to enhance mutual performance. However, existing FGL works primarily address graph data heterogeneity and perform incapable of graph task heterogeneity. To address the challenge, we propose a Federated Graph Prompt Learning (FedGPL) framework to efficiently enable prompt-based asymmetric graph knowledge transfer between multifaceted hetero","authors_text":"Hao Liu, Ruiqian Han, Zhuoning Guo","cross_cats":["cs.AI","cs.DC","cs.SI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-04T11:42:25Z","title":"Against Multifaceted Graph Heterogeneity via Asymmetric Federated Prompt Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.02003","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:bbb7b6eb37f6a503643cde552cc9d6d9ee4ed3007a50a0e74c5ce75826ac4029","target":"record","created_at":"2026-07-05T09:30:40Z","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":"ef4b28da0de8776bfd9072756ec9784539b162abb39a51d24b1faedb7df62ad3","cross_cats_sorted":["cs.AI","cs.DC","cs.SI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-04T11:42:25Z","title_canon_sha256":"3e09e3b34a9ac13847915ab2f6e1fa203084fbfd5518385ca7f09f2654d11530"},"schema_version":"1.0","source":{"id":"2411.02003","kind":"arxiv","version":1}},"canonical_sha256":"513183202bc261ee278a1331db62a16cddaf23b534f4ac4ae64dac9792e5c4f6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"513183202bc261ee278a1331db62a16cddaf23b534f4ac4ae64dac9792e5c4f6","first_computed_at":"2026-07-05T09:30:40.040060Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:30:40.040060Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"buT8Xppr6yJkbLUkCTfS+SK9n47k3M01mmPKAFs6+Yvi4QdltpQZe+uIUgWPxOrUqTosEUii4cHlKVjBzNmUCw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:30:40.040543Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.02003","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bbb7b6eb37f6a503643cde552cc9d6d9ee4ed3007a50a0e74c5ce75826ac4029","sha256:d2dbe362243bae015508db014b2d7342e2d9bcb73afc566a7f358f1ff00a8b6e"],"state_sha256":"798d30b3082115ba5c79efb39278cd964b514ae102164dcd79cd55fb5acca9a7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JXXcYFPEFkHJ+ryJHyNMHoA8oL7ZcI6wKjeWuKmyj9jjfvBN0t4kCR/Uwhll72e55psCATds9ciRC/JkeoXqDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T10:36:22.343765Z","bundle_sha256":"10318a4717c1a16dc1e44f5a99fd1798a5db8e7799792e5dacbb90bfcc5b9fda"}}