{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:3LSJVUONFLHKBMNEPORQSR6VZG","short_pith_number":"pith:3LSJVUON","canonical_record":{"source":{"id":"2007.16002","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-07-27T11:53:52Z","cross_cats_sorted":["cs.SI"],"title_canon_sha256":"c16bcaccf27704256309a142f3e24cca8656ca63a734422e50c62977e854daac","abstract_canon_sha256":"3135e4b490dbd9078ba803aacacfc7538e7901cc4ef8eb0d2ccbcc9d04281fae"},"schema_version":"1.0"},"canonical_sha256":"dae49ad1cd2acea0b1a47ba30947d5c9847e9f0763af2ee54126bdc99507979b","source":{"kind":"arxiv","id":"2007.16002","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2007.16002","created_at":"2026-07-05T01:23:40Z"},{"alias_kind":"arxiv_version","alias_value":"2007.16002v1","created_at":"2026-07-05T01:23:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.16002","created_at":"2026-07-05T01:23:40Z"},{"alias_kind":"pith_short_12","alias_value":"3LSJVUONFLHK","created_at":"2026-07-05T01:23:40Z"},{"alias_kind":"pith_short_16","alias_value":"3LSJVUONFLHKBMNE","created_at":"2026-07-05T01:23:40Z"},{"alias_kind":"pith_short_8","alias_value":"3LSJVUON","created_at":"2026-07-05T01:23:40Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:3LSJVUONFLHKBMNEPORQSR6VZG","target":"record","payload":{"canonical_record":{"source":{"id":"2007.16002","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-07-27T11:53:52Z","cross_cats_sorted":["cs.SI"],"title_canon_sha256":"c16bcaccf27704256309a142f3e24cca8656ca63a734422e50c62977e854daac","abstract_canon_sha256":"3135e4b490dbd9078ba803aacacfc7538e7901cc4ef8eb0d2ccbcc9d04281fae"},"schema_version":"1.0"},"canonical_sha256":"dae49ad1cd2acea0b1a47ba30947d5c9847e9f0763af2ee54126bdc99507979b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:23:40.655002Z","signature_b64":"PrItqneRWrTvS0R0Z4c0b/Odaon9bKg8mePjw2W+eQM8Li3VjgipeLmdh0LyOnkDDIiiG+2dfBOUkEIrAs5kAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dae49ad1cd2acea0b1a47ba30947d5c9847e9f0763af2ee54126bdc99507979b","last_reissued_at":"2026-07-05T01:23:40.654603Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:23:40.654603Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2007.16002","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":"2026-07-05T01:23:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6mCe3SP1ZL4p0HNuAQm/M6YBpAj0n5P63Bf6KMac6yQjCyRzAu8qJMvbRmXNQUs9NVlNkWrKkaWPF4kBpPi8Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T17:45:00.537050Z"},"content_sha256":"41d72333806bf1b8b4e7fb3d2d540122964c67e3d1a4b01852b46a20401fcd99","schema_version":"1.0","event_id":"sha256:41d72333806bf1b8b4e7fb3d2d540122964c67e3d1a4b01852b46a20401fcd99"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:3LSJVUONFLHKBMNEPORQSR6VZG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Graph Convolutional Networks using Heat Kernel for Semi-supervised Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SI"],"primary_cat":"cs.LG","authors_text":"Bingbing Xu, Huawei Shen, Keting Cen, Qi Cao, Xueqi Cheng","submitted_at":"2020-07-27T11:53:52Z","abstract_excerpt":"Graph convolutional networks gain remarkable success in semi-supervised learning on graph structured data. The key to graph-based semisupervised learning is capturing the smoothness of labels or features over nodes exerted by graph structure. Previous methods, spectral methods and spatial methods, devote to defining graph convolution as a weighted average over neighboring nodes, and then learn graph convolution kernels to leverage the smoothness to improve the performance of graph-based semi-supervised learning. One open challenge is how to determine appropriate neighborhood that reflects rele"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.16002","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/2007.16002/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":"2026-07-05T01:23:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CMLsNy6byYJWQmUHgN7SCkJRFofvr3ToNw/l3rCVbOuNcdsL6fIpO/NaadrDIUPaMQJ973sNXQOZ8RF7PwM4CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T17:45:00.537554Z"},"content_sha256":"2cc3e0cbc812d5c1f1b07e791a52125a0cd26131d6277e4261b8faa10b12471d","schema_version":"1.0","event_id":"sha256:2cc3e0cbc812d5c1f1b07e791a52125a0cd26131d6277e4261b8faa10b12471d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3LSJVUONFLHKBMNEPORQSR6VZG/bundle.json","state_url":"https://pith.science/pith/3LSJVUONFLHKBMNEPORQSR6VZG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3LSJVUONFLHKBMNEPORQSR6VZG/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":"2026-08-03T17:45:00Z","links":{"resolver":"https://pith.science/pith/3LSJVUONFLHKBMNEPORQSR6VZG","bundle":"https://pith.science/pith/3LSJVUONFLHKBMNEPORQSR6VZG/bundle.json","state":"https://pith.science/pith/3LSJVUONFLHKBMNEPORQSR6VZG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3LSJVUONFLHKBMNEPORQSR6VZG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:3LSJVUONFLHKBMNEPORQSR6VZG","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":"3135e4b490dbd9078ba803aacacfc7538e7901cc4ef8eb0d2ccbcc9d04281fae","cross_cats_sorted":["cs.SI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-07-27T11:53:52Z","title_canon_sha256":"c16bcaccf27704256309a142f3e24cca8656ca63a734422e50c62977e854daac"},"schema_version":"1.0","source":{"id":"2007.16002","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2007.16002","created_at":"2026-07-05T01:23:40Z"},{"alias_kind":"arxiv_version","alias_value":"2007.16002v1","created_at":"2026-07-05T01:23:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.16002","created_at":"2026-07-05T01:23:40Z"},{"alias_kind":"pith_short_12","alias_value":"3LSJVUONFLHK","created_at":"2026-07-05T01:23:40Z"},{"alias_kind":"pith_short_16","alias_value":"3LSJVUONFLHKBMNE","created_at":"2026-07-05T01:23:40Z"},{"alias_kind":"pith_short_8","alias_value":"3LSJVUON","created_at":"2026-07-05T01:23:40Z"}],"graph_snapshots":[{"event_id":"sha256:2cc3e0cbc812d5c1f1b07e791a52125a0cd26131d6277e4261b8faa10b12471d","target":"graph","created_at":"2026-07-05T01:23:40Z","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/2007.16002/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graph convolutional networks gain remarkable success in semi-supervised learning on graph structured data. The key to graph-based semisupervised learning is capturing the smoothness of labels or features over nodes exerted by graph structure. Previous methods, spectral methods and spatial methods, devote to defining graph convolution as a weighted average over neighboring nodes, and then learn graph convolution kernels to leverage the smoothness to improve the performance of graph-based semi-supervised learning. One open challenge is how to determine appropriate neighborhood that reflects rele","authors_text":"Bingbing Xu, Huawei Shen, Keting Cen, Qi Cao, Xueqi Cheng","cross_cats":["cs.SI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-07-27T11:53:52Z","title":"Graph Convolutional Networks using Heat Kernel for Semi-supervised Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.16002","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":"sha256:41d72333806bf1b8b4e7fb3d2d540122964c67e3d1a4b01852b46a20401fcd99","target":"record","created_at":"2026-07-05T01:23:40Z","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":"3135e4b490dbd9078ba803aacacfc7538e7901cc4ef8eb0d2ccbcc9d04281fae","cross_cats_sorted":["cs.SI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-07-27T11:53:52Z","title_canon_sha256":"c16bcaccf27704256309a142f3e24cca8656ca63a734422e50c62977e854daac"},"schema_version":"1.0","source":{"id":"2007.16002","kind":"arxiv","version":1}},"canonical_sha256":"dae49ad1cd2acea0b1a47ba30947d5c9847e9f0763af2ee54126bdc99507979b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"dae49ad1cd2acea0b1a47ba30947d5c9847e9f0763af2ee54126bdc99507979b","first_computed_at":"2026-07-05T01:23:40.654603Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:23:40.654603Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PrItqneRWrTvS0R0Z4c0b/Odaon9bKg8mePjw2W+eQM8Li3VjgipeLmdh0LyOnkDDIiiG+2dfBOUkEIrAs5kAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T01:23:40.655002Z","signed_message":"canonical_sha256_bytes"},"source_id":"2007.16002","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:41d72333806bf1b8b4e7fb3d2d540122964c67e3d1a4b01852b46a20401fcd99","sha256:2cc3e0cbc812d5c1f1b07e791a52125a0cd26131d6277e4261b8faa10b12471d"],"state_sha256":"359ac0b5603f724e14d2edd58a6c4050c6e5bbedb5abc0e906bf7d157c5c88af"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xGuBcU2RvtuWWlT8tbe+fGbM39qLTFcVRq903tlo6HkE3s2009k7nh5BQXpXFfKgBCruqVrPFQ9uKUIEfu9FCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T17:45:00.542789Z","bundle_sha256":"25e24ab0097c41a98e9280ccafd60fb9b50014c2ad1ecb12f67c85e42683086a"}}