{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:QOK7UN43USFXBXZWQEJESW3QE7","short_pith_number":"pith:QOK7UN43","canonical_record":{"source":{"id":"2003.05730","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-03-10T12:48:00Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"1da5e905a67deacf5e7d955e59706551b430bcfb77c10bc962245631a0c911e6","abstract_canon_sha256":"b712eaa9712d3c6ed62837905ade5f7a5170df2841fde4a4d13cf328488ae412"},"schema_version":"1.0"},"canonical_sha256":"8395fa379ba48b70df368112495b7027ce99c46aca25dc5d6fe8d8c6d55d647c","source":{"kind":"arxiv","id":"2003.05730","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2003.05730","created_at":"2026-07-05T04:11:22Z"},{"alias_kind":"arxiv_version","alias_value":"2003.05730v3","created_at":"2026-07-05T04:11:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.05730","created_at":"2026-07-05T04:11:22Z"},{"alias_kind":"pith_short_12","alias_value":"QOK7UN43USFX","created_at":"2026-07-05T04:11:22Z"},{"alias_kind":"pith_short_16","alias_value":"QOK7UN43USFXBXZW","created_at":"2026-07-05T04:11:22Z"},{"alias_kind":"pith_short_8","alias_value":"QOK7UN43","created_at":"2026-07-05T04:11:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:QOK7UN43USFXBXZWQEJESW3QE7","target":"record","payload":{"canonical_record":{"source":{"id":"2003.05730","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-03-10T12:48:00Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"1da5e905a67deacf5e7d955e59706551b430bcfb77c10bc962245631a0c911e6","abstract_canon_sha256":"b712eaa9712d3c6ed62837905ade5f7a5170df2841fde4a4d13cf328488ae412"},"schema_version":"1.0"},"canonical_sha256":"8395fa379ba48b70df368112495b7027ce99c46aca25dc5d6fe8d8c6d55d647c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:11:22.954588Z","signature_b64":"bhc50Rwfj2EU2hSuOr7QfPbXmPDbwk2tu5n7MxXgpcrLrQliJ1/P1lM+RLicm3MmJxV/GG1eClHZOHK35ezsCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8395fa379ba48b70df368112495b7027ce99c46aca25dc5d6fe8d8c6d55d647c","last_reissued_at":"2026-07-05T04:11:22.954103Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:11:22.954103Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2003.05730","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:11:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4GcGQuxh/FhfwEMEmsO8yWtLe7QXtjF1dIg7MAH+nzCWCNHv4pQagFqsYmTeiUb12frAuQAYshY7HBag6bW2Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T00:34:36.350290Z"},"content_sha256":"ff15037e6560fac6e5b44aa33d936066f43ec72899c20882ac75e845694b0f17","schema_version":"1.0","event_id":"sha256:ff15037e6560fac6e5b44aa33d936066f43ec72899c20882ac75e845694b0f17"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:QOK7UN43USFXBXZWQEJESW3QE7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Survey of Adversarial Learning on Graphs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Bingzhe Wu, Jiaying Peng, Jintang Li, Kun Xu, Liang Chen, Tao Xie, Xiangnan He, Zengxu Cao, Zibin Zheng","submitted_at":"2020-03-10T12:48:00Z","abstract_excerpt":"Deep learning models on graphs have achieved remarkable performance in various graph analysis tasks, e.g., node classification, link prediction, and graph clustering. However, they expose uncertainty and unreliability against the well-designed inputs, i.e., adversarial examples. Accordingly, a line of studies has emerged for both attack and defense addressed in different graph analysis tasks, leading to the arms race in graph adversarial learning. Despite the booming works, there still lacks a unified problem definition and a comprehensive review. To bridge this gap, we investigate and summari"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.05730","kind":"arxiv","version":3},"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/2003.05730/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":"2026-07-05T04:11:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9+2GqY85QGDFnE50Nzpb/OHAkRiXJ0l+2SdSewllXGOmVsIH7KoR2JMmm3paUUnVpXiV+Ope5z7n63/NRsGjBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T00:34:36.350869Z"},"content_sha256":"149ef2d3f0e3b2e4ae3fee42d4a5387f0443e9f9c695b66ebc2baa3d180b8599","schema_version":"1.0","event_id":"sha256:149ef2d3f0e3b2e4ae3fee42d4a5387f0443e9f9c695b66ebc2baa3d180b8599"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QOK7UN43USFXBXZWQEJESW3QE7/bundle.json","state_url":"https://pith.science/pith/QOK7UN43USFXBXZWQEJESW3QE7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QOK7UN43USFXBXZWQEJESW3QE7/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":"2026-08-10T00:34:36Z","links":{"resolver":"https://pith.science/pith/QOK7UN43USFXBXZWQEJESW3QE7","bundle":"https://pith.science/pith/QOK7UN43USFXBXZWQEJESW3QE7/bundle.json","state":"https://pith.science/pith/QOK7UN43USFXBXZWQEJESW3QE7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QOK7UN43USFXBXZWQEJESW3QE7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:QOK7UN43USFXBXZWQEJESW3QE7","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":"b712eaa9712d3c6ed62837905ade5f7a5170df2841fde4a4d13cf328488ae412","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-03-10T12:48:00Z","title_canon_sha256":"1da5e905a67deacf5e7d955e59706551b430bcfb77c10bc962245631a0c911e6"},"schema_version":"1.0","source":{"id":"2003.05730","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2003.05730","created_at":"2026-07-05T04:11:22Z"},{"alias_kind":"arxiv_version","alias_value":"2003.05730v3","created_at":"2026-07-05T04:11:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.05730","created_at":"2026-07-05T04:11:22Z"},{"alias_kind":"pith_short_12","alias_value":"QOK7UN43USFX","created_at":"2026-07-05T04:11:22Z"},{"alias_kind":"pith_short_16","alias_value":"QOK7UN43USFXBXZW","created_at":"2026-07-05T04:11:22Z"},{"alias_kind":"pith_short_8","alias_value":"QOK7UN43","created_at":"2026-07-05T04:11:22Z"}],"graph_snapshots":[{"event_id":"sha256:149ef2d3f0e3b2e4ae3fee42d4a5387f0443e9f9c695b66ebc2baa3d180b8599","target":"graph","created_at":"2026-07-05T04:11:22Z","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/2003.05730/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep learning models on graphs have achieved remarkable performance in various graph analysis tasks, e.g., node classification, link prediction, and graph clustering. However, they expose uncertainty and unreliability against the well-designed inputs, i.e., adversarial examples. Accordingly, a line of studies has emerged for both attack and defense addressed in different graph analysis tasks, leading to the arms race in graph adversarial learning. Despite the booming works, there still lacks a unified problem definition and a comprehensive review. To bridge this gap, we investigate and summari","authors_text":"Bingzhe Wu, Jiaying Peng, Jintang Li, Kun Xu, Liang Chen, Tao Xie, Xiangnan He, Zengxu Cao, Zibin Zheng","cross_cats":["cs.AI","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-03-10T12:48:00Z","title":"A Survey of Adversarial Learning on Graphs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.05730","kind":"arxiv","version":3},"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:ff15037e6560fac6e5b44aa33d936066f43ec72899c20882ac75e845694b0f17","target":"record","created_at":"2026-07-05T04:11:22Z","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":"b712eaa9712d3c6ed62837905ade5f7a5170df2841fde4a4d13cf328488ae412","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-03-10T12:48:00Z","title_canon_sha256":"1da5e905a67deacf5e7d955e59706551b430bcfb77c10bc962245631a0c911e6"},"schema_version":"1.0","source":{"id":"2003.05730","kind":"arxiv","version":3}},"canonical_sha256":"8395fa379ba48b70df368112495b7027ce99c46aca25dc5d6fe8d8c6d55d647c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8395fa379ba48b70df368112495b7027ce99c46aca25dc5d6fe8d8c6d55d647c","first_computed_at":"2026-07-05T04:11:22.954103Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:11:22.954103Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bhc50Rwfj2EU2hSuOr7QfPbXmPDbwk2tu5n7MxXgpcrLrQliJ1/P1lM+RLicm3MmJxV/GG1eClHZOHK35ezsCA==","signature_status":"signed_v1","signed_at":"2026-07-05T04:11:22.954588Z","signed_message":"canonical_sha256_bytes"},"source_id":"2003.05730","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ff15037e6560fac6e5b44aa33d936066f43ec72899c20882ac75e845694b0f17","sha256:149ef2d3f0e3b2e4ae3fee42d4a5387f0443e9f9c695b66ebc2baa3d180b8599"],"state_sha256":"37e0a1ba2c9c01dbc5562d9fb06b25adca9a1dc69a246fa1424c35fd3f482628"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"78JtWqmaUQ2oVZlkRwpQxZ4GKZyJ/cBUa0LQqRZtJ4ZIs9vdfOh6e+lXbBzGe/lBJjUf2T1nYHvEpfmq05yTDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T00:34:36.354518Z","bundle_sha256":"9e2b59828e689c2c4e5a1a7f6c02aa625293ba0d363f788f880efbea0cdea8c9"}}