{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:TMTLNAALUG732EXAECLHKASGQ2","short_pith_number":"pith:TMTLNAAL","canonical_record":{"source":{"id":"2006.13335","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-06-23T21:10:55Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"a83f24ffcdde15d9462ff178a1245c3c91600b36a56e127ccab0321de15ba14b","abstract_canon_sha256":"d7390affb5050c504462ab292dc81945283c865ed702d6f41f80bf45be92d4bf"},"schema_version":"1.0"},"canonical_sha256":"9b26b6800ba1bfbd12e02096750246868ae7cf985cf248f0bfde8d79d930aa22","source":{"kind":"arxiv","id":"2006.13335","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2006.13335","created_at":"2026-07-05T01:13:14Z"},{"alias_kind":"arxiv_version","alias_value":"2006.13335v1","created_at":"2026-07-05T01:13:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.13335","created_at":"2026-07-05T01:13:14Z"},{"alias_kind":"pith_short_12","alias_value":"TMTLNAALUG73","created_at":"2026-07-05T01:13:14Z"},{"alias_kind":"pith_short_16","alias_value":"TMTLNAALUG732EXA","created_at":"2026-07-05T01:13:14Z"},{"alias_kind":"pith_short_8","alias_value":"TMTLNAAL","created_at":"2026-07-05T01:13:14Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:TMTLNAALUG732EXAECLHKASGQ2","target":"record","payload":{"canonical_record":{"source":{"id":"2006.13335","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-06-23T21:10:55Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"a83f24ffcdde15d9462ff178a1245c3c91600b36a56e127ccab0321de15ba14b","abstract_canon_sha256":"d7390affb5050c504462ab292dc81945283c865ed702d6f41f80bf45be92d4bf"},"schema_version":"1.0"},"canonical_sha256":"9b26b6800ba1bfbd12e02096750246868ae7cf985cf248f0bfde8d79d930aa22","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:13:14.748488Z","signature_b64":"bB59NOXZGSfU0l6XLmVXuuFiehfKXe0dhMKsgg9ZYrIBBQIJq1cWcjpYUTUx4gvuWD5WuwWVUW2rVzsIUBuEBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9b26b6800ba1bfbd12e02096750246868ae7cf985cf248f0bfde8d79d930aa22","last_reissued_at":"2026-07-05T01:13:14.748043Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:13:14.748043Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2006.13335","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":"2026-07-05T01:13:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DHxKJ9sOLlixGhp7jn1uj/vfAgIgs/N2Sf003Oia8qdhhQKPNwv8x/D2s3m9qP7yXhTw1G4SrLSBjS6fDmcFDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-12T08:30:00.222182Z"},"content_sha256":"7f151595d2efa497908e2179978db6847310deb43b16585a8a820a02a69becb8","schema_version":"1.0","event_id":"sha256:7f151595d2efa497908e2179978db6847310deb43b16585a8a820a02a69becb8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:TMTLNAALUG732EXAECLHKASGQ2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Non-Parametric Graph Learning for Bayesian Graph Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Florence Regol, Mark Coates, Saber Malekmohammadi, Soumyasundar Pal, Yingxue Zhang, Yishi Xu","submitted_at":"2020-06-23T21:10:55Z","abstract_excerpt":"Graphs are ubiquitous in modelling relational structures. Recent endeavours in machine learning for graph-structured data have led to many architectures and learning algorithms. However, the graph used by these algorithms is often constructed based on inaccurate modelling assumptions and/or noisy data. As a result, it fails to represent the true relationships between nodes. A Bayesian framework which targets posterior inference of the graph by considering it as a random quantity can be beneficial. In this paper, we propose a novel non-parametric graph model for constructing the posterior distr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.13335","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/2006.13335/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":"2026-07-05T01:13:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"w3JhC5xLwGAEQKtDpi79jALP96Zi4/kuBHwwwoXmoOCVziZlnWfxwz22EuVRExp9iPJlJRSx362oYVgjbx8GAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-12T08:30:00.222577Z"},"content_sha256":"6f59e818c2c23ba1546192685c1fc6539ba8172473f251f7a9454540d711bbac","schema_version":"1.0","event_id":"sha256:6f59e818c2c23ba1546192685c1fc6539ba8172473f251f7a9454540d711bbac"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TMTLNAALUG732EXAECLHKASGQ2/bundle.json","state_url":"https://pith.science/pith/TMTLNAALUG732EXAECLHKASGQ2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TMTLNAALUG732EXAECLHKASGQ2/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":"2026-07-12T08:30:00Z","links":{"resolver":"https://pith.science/pith/TMTLNAALUG732EXAECLHKASGQ2","bundle":"https://pith.science/pith/TMTLNAALUG732EXAECLHKASGQ2/bundle.json","state":"https://pith.science/pith/TMTLNAALUG732EXAECLHKASGQ2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TMTLNAALUG732EXAECLHKASGQ2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:TMTLNAALUG732EXAECLHKASGQ2","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":"d7390affb5050c504462ab292dc81945283c865ed702d6f41f80bf45be92d4bf","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-06-23T21:10:55Z","title_canon_sha256":"a83f24ffcdde15d9462ff178a1245c3c91600b36a56e127ccab0321de15ba14b"},"schema_version":"1.0","source":{"id":"2006.13335","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2006.13335","created_at":"2026-07-05T01:13:14Z"},{"alias_kind":"arxiv_version","alias_value":"2006.13335v1","created_at":"2026-07-05T01:13:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.13335","created_at":"2026-07-05T01:13:14Z"},{"alias_kind":"pith_short_12","alias_value":"TMTLNAALUG73","created_at":"2026-07-05T01:13:14Z"},{"alias_kind":"pith_short_16","alias_value":"TMTLNAALUG732EXA","created_at":"2026-07-05T01:13:14Z"},{"alias_kind":"pith_short_8","alias_value":"TMTLNAAL","created_at":"2026-07-05T01:13:14Z"}],"graph_snapshots":[{"event_id":"sha256:6f59e818c2c23ba1546192685c1fc6539ba8172473f251f7a9454540d711bbac","target":"graph","created_at":"2026-07-05T01:13:14Z","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/2006.13335/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graphs are ubiquitous in modelling relational structures. Recent endeavours in machine learning for graph-structured data have led to many architectures and learning algorithms. However, the graph used by these algorithms is often constructed based on inaccurate modelling assumptions and/or noisy data. As a result, it fails to represent the true relationships between nodes. A Bayesian framework which targets posterior inference of the graph by considering it as a random quantity can be beneficial. In this paper, we propose a novel non-parametric graph model for constructing the posterior distr","authors_text":"Florence Regol, Mark Coates, Saber Malekmohammadi, Soumyasundar Pal, Yingxue Zhang, Yishi Xu","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-06-23T21:10:55Z","title":"Non-Parametric Graph Learning for Bayesian Graph Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.13335","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":"sha256:7f151595d2efa497908e2179978db6847310deb43b16585a8a820a02a69becb8","target":"record","created_at":"2026-07-05T01:13:14Z","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":"d7390affb5050c504462ab292dc81945283c865ed702d6f41f80bf45be92d4bf","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-06-23T21:10:55Z","title_canon_sha256":"a83f24ffcdde15d9462ff178a1245c3c91600b36a56e127ccab0321de15ba14b"},"schema_version":"1.0","source":{"id":"2006.13335","kind":"arxiv","version":1}},"canonical_sha256":"9b26b6800ba1bfbd12e02096750246868ae7cf985cf248f0bfde8d79d930aa22","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9b26b6800ba1bfbd12e02096750246868ae7cf985cf248f0bfde8d79d930aa22","first_computed_at":"2026-07-05T01:13:14.748043Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:13:14.748043Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bB59NOXZGSfU0l6XLmVXuuFiehfKXe0dhMKsgg9ZYrIBBQIJq1cWcjpYUTUx4gvuWD5WuwWVUW2rVzsIUBuEBw==","signature_status":"signed_v1","signed_at":"2026-07-05T01:13:14.748488Z","signed_message":"canonical_sha256_bytes"},"source_id":"2006.13335","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7f151595d2efa497908e2179978db6847310deb43b16585a8a820a02a69becb8","sha256:6f59e818c2c23ba1546192685c1fc6539ba8172473f251f7a9454540d711bbac"],"state_sha256":"4e73e9dc22e9a0d4d6f331f4200ec964f751f7bb1c0bbc4e603123d03b0b3b8a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FkGC9l+v9q6h6MpjW/u+VVZrI1ePBf2+PvhhSsvxIvAcRZuk5cQtTeL0GlGvqXELeUYIfL3Ey7ggFvF75eBRDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-12T08:30:00.224728Z","bundle_sha256":"c51941a6c00a053ec267fee0a5717cffcd29ed70f0446062f44a406e0a5b4075"}}