{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:3P2PTHHIUVI73FN77KRQZO3JLV","short_pith_number":"pith:3P2PTHHI","canonical_record":{"source":{"id":"2210.00538","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-10-02T14:41:02Z","cross_cats_sorted":["cs.CR"],"title_canon_sha256":"6b388dcc712105393d2bdd26c5ec1d5cac28456baef32ee38f96ad132b7b9c17","abstract_canon_sha256":"f0de2e7f456b0fc607a9b16eb4bac7f16139267a17107683644a4fc07481bc1e"},"schema_version":"1.0"},"canonical_sha256":"dbf4f99ce8a551fd95bffaa30cbb695d7c5c281b31e8db158580e1882b016073","source":{"kind":"arxiv","id":"2210.00538","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.00538","created_at":"2026-07-05T05:04:31Z"},{"alias_kind":"arxiv_version","alias_value":"2210.00538v2","created_at":"2026-07-05T05:04:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.00538","created_at":"2026-07-05T05:04:31Z"},{"alias_kind":"pith_short_12","alias_value":"3P2PTHHIUVI7","created_at":"2026-07-05T05:04:31Z"},{"alias_kind":"pith_short_16","alias_value":"3P2PTHHIUVI73FN7","created_at":"2026-07-05T05:04:31Z"},{"alias_kind":"pith_short_8","alias_value":"3P2PTHHI","created_at":"2026-07-05T05:04:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:3P2PTHHIUVI73FN77KRQZO3JLV","target":"record","payload":{"canonical_record":{"source":{"id":"2210.00538","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-10-02T14:41:02Z","cross_cats_sorted":["cs.CR"],"title_canon_sha256":"6b388dcc712105393d2bdd26c5ec1d5cac28456baef32ee38f96ad132b7b9c17","abstract_canon_sha256":"f0de2e7f456b0fc607a9b16eb4bac7f16139267a17107683644a4fc07481bc1e"},"schema_version":"1.0"},"canonical_sha256":"dbf4f99ce8a551fd95bffaa30cbb695d7c5c281b31e8db158580e1882b016073","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:04:31.429347Z","signature_b64":"+ggjQMeuFv8KYXaxWQFD/2zcN95i8UYtysnP9s+Qun9/KsTPlz0mHSbsV06pvlM9sDhFQJV+gguVjdK8nFF9BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dbf4f99ce8a551fd95bffaa30cbb695d7c5c281b31e8db158580e1882b016073","last_reissued_at":"2026-07-05T05:04:31.428958Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:04:31.428958Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2210.00538","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":"2026-07-05T05:04:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EVnqk2RivbtSsu8ISK/vy5gTESv7TXkyw8nBEzqbNkTNKxJZdJ2d5l3ogxSvIn7rVdDT3fqLoNUla0GQgUVwDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T22:52:41.896044Z"},"content_sha256":"770fb27a0ddbc15d6cbae8d193d3355f1f8f5254af27d9b3b4a3f2fd17e12853","schema_version":"1.0","event_id":"sha256:770fb27a0ddbc15d6cbae8d193d3355f1f8f5254af27d9b3b4a3f2fd17e12853"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:3P2PTHHIUVI73FN77KRQZO3JLV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Heterogeneous Graph Neural Network for Privacy-Preserving Recommendation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR"],"primary_cat":"cs.LG","authors_text":"Hao Peng, Jia Wu, Jinyan Wang, Qingyun Sun, Xianxian Li, Xingcheng Fu, Yuecen Wei","submitted_at":"2022-10-02T14:41:02Z","abstract_excerpt":"Social networks are considered to be heterogeneous graph neural networks (HGNNs) with deep learning technological advances. HGNNs, compared to homogeneous data, absorb various aspects of information about individuals in the training stage. That means more information has been covered in the learning result, especially sensitive information. However, the privacy-preserving methods on homogeneous graphs only preserve the same type of node attributes or relationships, which cannot effectively work on heterogeneous graphs due to the complexity. To address this issue, we propose a novel heterogeneo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.00538","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/2210.00538/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":"2026-07-05T05:04:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3N639wkBmbiTj6IVQVl8ZuAQeNbZfgulNg0oBcoJ1CmEj0nEpV5Sd+rdNhqTIQvoAqMtbJh/fjwjPjFB4mEPAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T22:52:41.896670Z"},"content_sha256":"e8233413bf98a3bff712fdaadca5f10fff8818c6035b0e00d34a043d61b352a3","schema_version":"1.0","event_id":"sha256:e8233413bf98a3bff712fdaadca5f10fff8818c6035b0e00d34a043d61b352a3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3P2PTHHIUVI73FN77KRQZO3JLV/bundle.json","state_url":"https://pith.science/pith/3P2PTHHIUVI73FN77KRQZO3JLV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3P2PTHHIUVI73FN77KRQZO3JLV/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":"2026-08-14T22:52:41Z","links":{"resolver":"https://pith.science/pith/3P2PTHHIUVI73FN77KRQZO3JLV","bundle":"https://pith.science/pith/3P2PTHHIUVI73FN77KRQZO3JLV/bundle.json","state":"https://pith.science/pith/3P2PTHHIUVI73FN77KRQZO3JLV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3P2PTHHIUVI73FN77KRQZO3JLV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:3P2PTHHIUVI73FN77KRQZO3JLV","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":"f0de2e7f456b0fc607a9b16eb4bac7f16139267a17107683644a4fc07481bc1e","cross_cats_sorted":["cs.CR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-10-02T14:41:02Z","title_canon_sha256":"6b388dcc712105393d2bdd26c5ec1d5cac28456baef32ee38f96ad132b7b9c17"},"schema_version":"1.0","source":{"id":"2210.00538","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.00538","created_at":"2026-07-05T05:04:31Z"},{"alias_kind":"arxiv_version","alias_value":"2210.00538v2","created_at":"2026-07-05T05:04:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.00538","created_at":"2026-07-05T05:04:31Z"},{"alias_kind":"pith_short_12","alias_value":"3P2PTHHIUVI7","created_at":"2026-07-05T05:04:31Z"},{"alias_kind":"pith_short_16","alias_value":"3P2PTHHIUVI73FN7","created_at":"2026-07-05T05:04:31Z"},{"alias_kind":"pith_short_8","alias_value":"3P2PTHHI","created_at":"2026-07-05T05:04:31Z"}],"graph_snapshots":[{"event_id":"sha256:e8233413bf98a3bff712fdaadca5f10fff8818c6035b0e00d34a043d61b352a3","target":"graph","created_at":"2026-07-05T05:04:31Z","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/2210.00538/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Social networks are considered to be heterogeneous graph neural networks (HGNNs) with deep learning technological advances. HGNNs, compared to homogeneous data, absorb various aspects of information about individuals in the training stage. That means more information has been covered in the learning result, especially sensitive information. However, the privacy-preserving methods on homogeneous graphs only preserve the same type of node attributes or relationships, which cannot effectively work on heterogeneous graphs due to the complexity. To address this issue, we propose a novel heterogeneo","authors_text":"Hao Peng, Jia Wu, Jinyan Wang, Qingyun Sun, Xianxian Li, Xingcheng Fu, Yuecen Wei","cross_cats":["cs.CR"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-10-02T14:41:02Z","title":"Heterogeneous Graph Neural Network for Privacy-Preserving Recommendation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.00538","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":"sha256:770fb27a0ddbc15d6cbae8d193d3355f1f8f5254af27d9b3b4a3f2fd17e12853","target":"record","created_at":"2026-07-05T05:04:31Z","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":"f0de2e7f456b0fc607a9b16eb4bac7f16139267a17107683644a4fc07481bc1e","cross_cats_sorted":["cs.CR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-10-02T14:41:02Z","title_canon_sha256":"6b388dcc712105393d2bdd26c5ec1d5cac28456baef32ee38f96ad132b7b9c17"},"schema_version":"1.0","source":{"id":"2210.00538","kind":"arxiv","version":2}},"canonical_sha256":"dbf4f99ce8a551fd95bffaa30cbb695d7c5c281b31e8db158580e1882b016073","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"dbf4f99ce8a551fd95bffaa30cbb695d7c5c281b31e8db158580e1882b016073","first_computed_at":"2026-07-05T05:04:31.428958Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:04:31.428958Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+ggjQMeuFv8KYXaxWQFD/2zcN95i8UYtysnP9s+Qun9/KsTPlz0mHSbsV06pvlM9sDhFQJV+gguVjdK8nFF9BQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:04:31.429347Z","signed_message":"canonical_sha256_bytes"},"source_id":"2210.00538","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:770fb27a0ddbc15d6cbae8d193d3355f1f8f5254af27d9b3b4a3f2fd17e12853","sha256:e8233413bf98a3bff712fdaadca5f10fff8818c6035b0e00d34a043d61b352a3"],"state_sha256":"09b72353a5c3f99c350fd45e52ee4aadc0f951925043ba1865dfc288034273bb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SAILbmjfamrAgGFYP2XPymYq1QXTjBTPp8zHddlqSZTK+KB/5JcikFiKGdvvQ19Cr91q7+8bAuEps+KXcMnpAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-14T22:52:41.902868Z","bundle_sha256":"0305c8b2255cc2166289a492a0534e9efd0c5949fced0e9f48e04b1b69bd332c"}}