{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:BFGC3K4JCYJGZNAVOOU3YIHYCG","short_pith_number":"pith:BFGC3K4J","canonical_record":{"source":{"id":"2303.10993","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-03-20T10:21:29Z","cross_cats_sorted":[],"title_canon_sha256":"89800991a35ec05d2e9c55ab72119765bcb94f858ea21d5118a5b52c9b0ea21f","abstract_canon_sha256":"ffb5726c4a52c888f700d9b0b429ce91ee59764b426af7c1b41a9ec481829368"},"schema_version":"1.0"},"canonical_sha256":"094c2dab8916126cb41573a9bc20f81184a3e090d4f23f27d8a850f8ef9148f9","source":{"kind":"arxiv","id":"2303.10993","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2303.10993","created_at":"2026-07-05T05:52:38Z"},{"alias_kind":"arxiv_version","alias_value":"2303.10993v1","created_at":"2026-07-05T05:52:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.10993","created_at":"2026-07-05T05:52:38Z"},{"alias_kind":"pith_short_12","alias_value":"BFGC3K4JCYJG","created_at":"2026-07-05T05:52:38Z"},{"alias_kind":"pith_short_16","alias_value":"BFGC3K4JCYJGZNAV","created_at":"2026-07-05T05:52:38Z"},{"alias_kind":"pith_short_8","alias_value":"BFGC3K4J","created_at":"2026-07-05T05:52:38Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:BFGC3K4JCYJGZNAVOOU3YIHYCG","target":"record","payload":{"canonical_record":{"source":{"id":"2303.10993","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-03-20T10:21:29Z","cross_cats_sorted":[],"title_canon_sha256":"89800991a35ec05d2e9c55ab72119765bcb94f858ea21d5118a5b52c9b0ea21f","abstract_canon_sha256":"ffb5726c4a52c888f700d9b0b429ce91ee59764b426af7c1b41a9ec481829368"},"schema_version":"1.0"},"canonical_sha256":"094c2dab8916126cb41573a9bc20f81184a3e090d4f23f27d8a850f8ef9148f9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:52:38.916501Z","signature_b64":"Urx9ky8S3KvwCUQw0r4Q/h5ljL2kwZpGUhG4LvKTDrXKS+lONN6KcQEyg1DthNpdvMNMspJM/R/bGS1tjCrHBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"094c2dab8916126cb41573a9bc20f81184a3e090d4f23f27d8a850f8ef9148f9","last_reissued_at":"2026-07-05T05:52:38.916099Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:52:38.916099Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2303.10993","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":"2026-07-05T05:52:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OWfKvJC/KGBv1vx97BE4ZgRvqikzpXlehajnI2DQzmfc70ZktOIwMD+igFK4U/llgDQmKquKw+S+ggobri6vAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T18:36:38.244627Z"},"content_sha256":"b6a4daa3bfa1e29116f4836ab8ed040e7c70c1e8592b65f9875f465bc960a7d9","schema_version":"1.0","event_id":"sha256:b6a4daa3bfa1e29116f4836ab8ed040e7c70c1e8592b65f9875f465bc960a7d9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:BFGC3K4JCYJGZNAVOOU3YIHYCG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Survey on Oversmoothing in Graph Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Michael M. Bronstein, Siddhartha Mishra, T. Konstantin Rusch","submitted_at":"2023-03-20T10:21:29Z","abstract_excerpt":"Node features of graph neural networks (GNNs) tend to become more similar with the increase of the network depth. This effect is known as over-smoothing, which we axiomatically define as the exponential convergence of suitable similarity measures on the node features. Our definition unifies previous approaches and gives rise to new quantitative measures of over-smoothing. Moreover, we empirically demonstrate this behavior for several over-smoothing measures on different graphs (small-, medium-, and large-scale). We also review several approaches for mitigating over-smoothing and empirically te"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.10993","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/2303.10993/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":"2026-07-05T05:52:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gcNPmy1fZlfWZ0XxImZpMKg6akm+10USbQ3oqyFvyT/a9IIYgiFe88yNtQRYiJSMoyJTvlBGgF13fzplg41iDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T18:36:38.245185Z"},"content_sha256":"0238e71e4651dca9e341d55613564d33fcb04ca0840bc08b9871a389f484a710","schema_version":"1.0","event_id":"sha256:0238e71e4651dca9e341d55613564d33fcb04ca0840bc08b9871a389f484a710"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BFGC3K4JCYJGZNAVOOU3YIHYCG/bundle.json","state_url":"https://pith.science/pith/BFGC3K4JCYJGZNAVOOU3YIHYCG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BFGC3K4JCYJGZNAVOOU3YIHYCG/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":"2026-08-07T18:36:38Z","links":{"resolver":"https://pith.science/pith/BFGC3K4JCYJGZNAVOOU3YIHYCG","bundle":"https://pith.science/pith/BFGC3K4JCYJGZNAVOOU3YIHYCG/bundle.json","state":"https://pith.science/pith/BFGC3K4JCYJGZNAVOOU3YIHYCG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BFGC3K4JCYJGZNAVOOU3YIHYCG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:BFGC3K4JCYJGZNAVOOU3YIHYCG","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":"ffb5726c4a52c888f700d9b0b429ce91ee59764b426af7c1b41a9ec481829368","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-03-20T10:21:29Z","title_canon_sha256":"89800991a35ec05d2e9c55ab72119765bcb94f858ea21d5118a5b52c9b0ea21f"},"schema_version":"1.0","source":{"id":"2303.10993","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2303.10993","created_at":"2026-07-05T05:52:38Z"},{"alias_kind":"arxiv_version","alias_value":"2303.10993v1","created_at":"2026-07-05T05:52:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.10993","created_at":"2026-07-05T05:52:38Z"},{"alias_kind":"pith_short_12","alias_value":"BFGC3K4JCYJG","created_at":"2026-07-05T05:52:38Z"},{"alias_kind":"pith_short_16","alias_value":"BFGC3K4JCYJGZNAV","created_at":"2026-07-05T05:52:38Z"},{"alias_kind":"pith_short_8","alias_value":"BFGC3K4J","created_at":"2026-07-05T05:52:38Z"}],"graph_snapshots":[{"event_id":"sha256:0238e71e4651dca9e341d55613564d33fcb04ca0840bc08b9871a389f484a710","target":"graph","created_at":"2026-07-05T05:52:38Z","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/2303.10993/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Node features of graph neural networks (GNNs) tend to become more similar with the increase of the network depth. This effect is known as over-smoothing, which we axiomatically define as the exponential convergence of suitable similarity measures on the node features. Our definition unifies previous approaches and gives rise to new quantitative measures of over-smoothing. Moreover, we empirically demonstrate this behavior for several over-smoothing measures on different graphs (small-, medium-, and large-scale). We also review several approaches for mitigating over-smoothing and empirically te","authors_text":"Michael M. Bronstein, Siddhartha Mishra, T. Konstantin Rusch","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-03-20T10:21:29Z","title":"A Survey on Oversmoothing in Graph Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.10993","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:b6a4daa3bfa1e29116f4836ab8ed040e7c70c1e8592b65f9875f465bc960a7d9","target":"record","created_at":"2026-07-05T05:52:38Z","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":"ffb5726c4a52c888f700d9b0b429ce91ee59764b426af7c1b41a9ec481829368","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-03-20T10:21:29Z","title_canon_sha256":"89800991a35ec05d2e9c55ab72119765bcb94f858ea21d5118a5b52c9b0ea21f"},"schema_version":"1.0","source":{"id":"2303.10993","kind":"arxiv","version":1}},"canonical_sha256":"094c2dab8916126cb41573a9bc20f81184a3e090d4f23f27d8a850f8ef9148f9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"094c2dab8916126cb41573a9bc20f81184a3e090d4f23f27d8a850f8ef9148f9","first_computed_at":"2026-07-05T05:52:38.916099Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:52:38.916099Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Urx9ky8S3KvwCUQw0r4Q/h5ljL2kwZpGUhG4LvKTDrXKS+lONN6KcQEyg1DthNpdvMNMspJM/R/bGS1tjCrHBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:52:38.916501Z","signed_message":"canonical_sha256_bytes"},"source_id":"2303.10993","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b6a4daa3bfa1e29116f4836ab8ed040e7c70c1e8592b65f9875f465bc960a7d9","sha256:0238e71e4651dca9e341d55613564d33fcb04ca0840bc08b9871a389f484a710"],"state_sha256":"256bcb33a8c3927e69a15a55b996300d65682de6c25db4b8afac77629e590ba1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"y+riqj1Z6gIylXyKlrVuf+wFnEeFZkpL/MLuaEL23e+Hz3Pw8jYKU4ujE0d9EJ/Bk0O3yX+ebwZfAWSFaI/2AA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T18:36:38.249893Z","bundle_sha256":"b4d5ea3f7fd139ba7bee018c6211898d9beab47f22e4742028a57a1602e98b6f"}}