{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:YRWM36DFJ5W66N7DOA4VD2J4LC","short_pith_number":"pith:YRWM36DF","canonical_record":{"source":{"id":"2112.08903","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-12-16T14:22:13Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"4629fcdf928561d70679bed1c9fa081ff1741fd235aa0996bde12c8e5147a9e7","abstract_canon_sha256":"a68191a3b8fee5a78abbaa4c2890cd89aa0fdfb7c99fb0395b61af9c93b53bff"},"schema_version":"1.0"},"canonical_sha256":"c46ccdf8654f6def37e3703951e93c58b807ecc6bdf76844c05a493e3662ff49","source":{"kind":"arxiv","id":"2112.08903","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2112.08903","created_at":"2026-07-05T03:41:34Z"},{"alias_kind":"arxiv_version","alias_value":"2112.08903v1","created_at":"2026-07-05T03:41:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.08903","created_at":"2026-07-05T03:41:34Z"},{"alias_kind":"pith_short_12","alias_value":"YRWM36DFJ5W6","created_at":"2026-07-05T03:41:34Z"},{"alias_kind":"pith_short_16","alias_value":"YRWM36DFJ5W66N7D","created_at":"2026-07-05T03:41:34Z"},{"alias_kind":"pith_short_8","alias_value":"YRWM36DF","created_at":"2026-07-05T03:41:34Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:YRWM36DFJ5W66N7DOA4VD2J4LC","target":"record","payload":{"canonical_record":{"source":{"id":"2112.08903","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-12-16T14:22:13Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"4629fcdf928561d70679bed1c9fa081ff1741fd235aa0996bde12c8e5147a9e7","abstract_canon_sha256":"a68191a3b8fee5a78abbaa4c2890cd89aa0fdfb7c99fb0395b61af9c93b53bff"},"schema_version":"1.0"},"canonical_sha256":"c46ccdf8654f6def37e3703951e93c58b807ecc6bdf76844c05a493e3662ff49","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:41:34.137930Z","signature_b64":"K2NhjA3deG6uXMeOyikuvd0tbiKlfAj0EJ6M4UVOWJw2uJPiTT8n4amCQ7oSIrnB9z3dWisEdHzwf+LaeobwAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c46ccdf8654f6def37e3703951e93c58b807ecc6bdf76844c05a493e3662ff49","last_reissued_at":"2026-07-05T03:41:34.137422Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:41:34.137422Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2112.08903","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":"2026-07-05T03:41:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"r7b4XO64vE2I+BA24F8Tgsz6yU33tLe9WRmGLruI4DVJ7iW1aNRcBC01KcQH6DM8DFRZg6fMFHE7D0xlHPmiAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T06:13:49.936337Z"},"content_sha256":"d3bd59339a509cf206ea88c443862607366437f64e46224c3e04efcc5bfd3ac0","schema_version":"1.0","event_id":"sha256:d3bd59339a509cf206ea88c443862607366437f64e46224c3e04efcc5bfd3ac0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:YRWM36DFJ5W66N7DOA4VD2J4LC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Graph Structure Learning with Variational Information Bottleneck","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Cheng Ji, Hao Peng, Jianxin Li, Jia Wu, Philip S. Yu, Qingyun Sun, Xingcheng Fu","submitted_at":"2021-12-16T14:22:13Z","abstract_excerpt":"Graph Neural Networks (GNNs) have shown promising results on a broad spectrum of applications. Most empirical studies of GNNs directly take the observed graph as input, assuming the observed structure perfectly depicts the accurate and complete relations between nodes. However, graphs in the real world are inevitably noisy or incomplete, which could even exacerbate the quality of graph representations. In this work, we propose a novel Variational Information Bottleneck guided Graph Structure Learning framework, namely VIB-GSL, in the perspective of information theory. VIB-GSL advances the Info"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.08903","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/2112.08903/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":"2026-07-05T03:41:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PqAPpbjHTSzpvEReOk+D6BwIxIoF1FvAa3CjnsayJOeSDxP+wP0tRdoCKSy7zHKGF50D7jIKTcA8rE2vbQe8AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T06:13:49.936816Z"},"content_sha256":"e2fcd5047e3bbbcb51846ee2f7f466df1581bda7717e4a591596bb85239770fd","schema_version":"1.0","event_id":"sha256:e2fcd5047e3bbbcb51846ee2f7f466df1581bda7717e4a591596bb85239770fd"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YRWM36DFJ5W66N7DOA4VD2J4LC/bundle.json","state_url":"https://pith.science/pith/YRWM36DFJ5W66N7DOA4VD2J4LC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YRWM36DFJ5W66N7DOA4VD2J4LC/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":"2026-08-15T06:13:49Z","links":{"resolver":"https://pith.science/pith/YRWM36DFJ5W66N7DOA4VD2J4LC","bundle":"https://pith.science/pith/YRWM36DFJ5W66N7DOA4VD2J4LC/bundle.json","state":"https://pith.science/pith/YRWM36DFJ5W66N7DOA4VD2J4LC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YRWM36DFJ5W66N7DOA4VD2J4LC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:YRWM36DFJ5W66N7DOA4VD2J4LC","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":"a68191a3b8fee5a78abbaa4c2890cd89aa0fdfb7c99fb0395b61af9c93b53bff","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-12-16T14:22:13Z","title_canon_sha256":"4629fcdf928561d70679bed1c9fa081ff1741fd235aa0996bde12c8e5147a9e7"},"schema_version":"1.0","source":{"id":"2112.08903","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2112.08903","created_at":"2026-07-05T03:41:34Z"},{"alias_kind":"arxiv_version","alias_value":"2112.08903v1","created_at":"2026-07-05T03:41:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.08903","created_at":"2026-07-05T03:41:34Z"},{"alias_kind":"pith_short_12","alias_value":"YRWM36DFJ5W6","created_at":"2026-07-05T03:41:34Z"},{"alias_kind":"pith_short_16","alias_value":"YRWM36DFJ5W66N7D","created_at":"2026-07-05T03:41:34Z"},{"alias_kind":"pith_short_8","alias_value":"YRWM36DF","created_at":"2026-07-05T03:41:34Z"}],"graph_snapshots":[{"event_id":"sha256:e2fcd5047e3bbbcb51846ee2f7f466df1581bda7717e4a591596bb85239770fd","target":"graph","created_at":"2026-07-05T03:41:34Z","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/2112.08903/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graph Neural Networks (GNNs) have shown promising results on a broad spectrum of applications. Most empirical studies of GNNs directly take the observed graph as input, assuming the observed structure perfectly depicts the accurate and complete relations between nodes. However, graphs in the real world are inevitably noisy or incomplete, which could even exacerbate the quality of graph representations. In this work, we propose a novel Variational Information Bottleneck guided Graph Structure Learning framework, namely VIB-GSL, in the perspective of information theory. VIB-GSL advances the Info","authors_text":"Cheng Ji, Hao Peng, Jianxin Li, Jia Wu, Philip S. Yu, Qingyun Sun, Xingcheng Fu","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-12-16T14:22:13Z","title":"Graph Structure Learning with Variational Information Bottleneck"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.08903","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:d3bd59339a509cf206ea88c443862607366437f64e46224c3e04efcc5bfd3ac0","target":"record","created_at":"2026-07-05T03:41:34Z","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":"a68191a3b8fee5a78abbaa4c2890cd89aa0fdfb7c99fb0395b61af9c93b53bff","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-12-16T14:22:13Z","title_canon_sha256":"4629fcdf928561d70679bed1c9fa081ff1741fd235aa0996bde12c8e5147a9e7"},"schema_version":"1.0","source":{"id":"2112.08903","kind":"arxiv","version":1}},"canonical_sha256":"c46ccdf8654f6def37e3703951e93c58b807ecc6bdf76844c05a493e3662ff49","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c46ccdf8654f6def37e3703951e93c58b807ecc6bdf76844c05a493e3662ff49","first_computed_at":"2026-07-05T03:41:34.137422Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:41:34.137422Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"K2NhjA3deG6uXMeOyikuvd0tbiKlfAj0EJ6M4UVOWJw2uJPiTT8n4amCQ7oSIrnB9z3dWisEdHzwf+LaeobwAw==","signature_status":"signed_v1","signed_at":"2026-07-05T03:41:34.137930Z","signed_message":"canonical_sha256_bytes"},"source_id":"2112.08903","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d3bd59339a509cf206ea88c443862607366437f64e46224c3e04efcc5bfd3ac0","sha256:e2fcd5047e3bbbcb51846ee2f7f466df1581bda7717e4a591596bb85239770fd"],"state_sha256":"c3b54dc04c1f3888bc7a266d5a7f94fc725224ae46a9b7ba51e48736425a7a74"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"f6Pp8Iz6nMW6+rgGqFZH3NP3GrU0RHmkUVF/Ww/P6NLhmcBUks5PbwRrPdbWiqW1hCDi2MFHM3DzpRT4AC4EDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-15T06:13:49.939822Z","bundle_sha256":"c4f51b90b565e53e973697ca56b06c41aabe1a770703febb2994a9e3b4ba6b88"}}