{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:A5EP7OYTTAEM7UVBWGT2BGFGIR","short_pith_number":"pith:A5EP7OYT","canonical_record":{"source":{"id":"1806.02193","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-06-06T14:04:28Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"89f8e07b20181f718eef89753eb870d127599c16d517d96bd46f04ef9a7dfba3","abstract_canon_sha256":"877b4309b0c661cf1715fdb48c0f3aad6c01013c50c76efbef1ee1fb92d1d08b"},"schema_version":"1.0"},"canonical_sha256":"0748ffbb139808cfd2a1b1a7a098a6446a73a7b879ecc11f1186a4a63662845d","source":{"kind":"arxiv","id":"1806.02193","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.02193","created_at":"2026-07-05T00:49:33Z"},{"alias_kind":"arxiv_version","alias_value":"1806.02193v2","created_at":"2026-07-05T00:49:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.02193","created_at":"2026-07-05T00:49:33Z"},{"alias_kind":"pith_short_12","alias_value":"A5EP7OYTTAEM","created_at":"2026-07-05T00:49:33Z"},{"alias_kind":"pith_short_16","alias_value":"A5EP7OYTTAEM7UVB","created_at":"2026-07-05T00:49:33Z"},{"alias_kind":"pith_short_8","alias_value":"A5EP7OYT","created_at":"2026-07-05T00:49:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:A5EP7OYTTAEM7UVBWGT2BGFGIR","target":"record","payload":{"canonical_record":{"source":{"id":"1806.02193","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-06-06T14:04:28Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"89f8e07b20181f718eef89753eb870d127599c16d517d96bd46f04ef9a7dfba3","abstract_canon_sha256":"877b4309b0c661cf1715fdb48c0f3aad6c01013c50c76efbef1ee1fb92d1d08b"},"schema_version":"1.0"},"canonical_sha256":"0748ffbb139808cfd2a1b1a7a098a6446a73a7b879ecc11f1186a4a63662845d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:49:33.909013Z","signature_b64":"QbO2QWA1sAcmdZJ64Sds/IYjABRZoOrttRP7GPtXJqoZQswp6djf0GRRAC5EzTC59KsoV3hKck49gRyrjC/GAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0748ffbb139808cfd2a1b1a7a098a6446a73a7b879ecc11f1186a4a63662845d","last_reissued_at":"2026-07-05T00:49:33.908477Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:49:33.908477Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1806.02193","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:49:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fHE5Zfc+E74XHHxa3JlQAYL55N3mfunPAdE1+Ji35GOeXU6bz0RO60jAXNK+28c6aAmQ9noMD16PP8/1x9IDBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T15:41:32.067747Z"},"content_sha256":"014480c64b1e1a166f23adb5124070fd42059ea4c80107dc37fd27c34a792d52","schema_version":"1.0","event_id":"sha256:014480c64b1e1a166f23adb5124070fd42059ea4c80107dc37fd27c34a792d52"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:A5EP7OYTTAEM7UVBWGT2BGFGIR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"GraKeL: A Graph Kernel Library in Python","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Christos Giatsidis, Giannis Nikolentzos, Giannis Siglidis, Konstantinos Skianis, Michalis Vazirgiannis, Stratis Limnios","submitted_at":"2018-06-06T14:04:28Z","abstract_excerpt":"The problem of accurately measuring the similarity between graphs is at the core of many applications in a variety of disciplines. Graph kernels have recently emerged as a promising approach to this problem. There are now many kernels, each focusing on different structural aspects of graphs. Here, we present GraKeL, a library that unifies several graph kernels into a common framework. The library is written in Python and adheres to the scikit-learn interface. It is simple to use and can be naturally combined with scikit-learn's modules to build a complete machine learning pipeline for tasks su"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.02193","kind":"arxiv","version":2},"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/1806.02193/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":"2026-07-05T00:49:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zpAWCMpr6EIlOnBbECYBerXmIJ2ffFezB11FRBAt+MLll2wjE/fYhX2SctQ0PRjXeYQ4OftFaO3Q2BAsJ3leDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T15:41:32.068233Z"},"content_sha256":"d56d83713c2a6888c7e665edba1eb6ed9e303d471d031f75bb36a5c243ae12a0","schema_version":"1.0","event_id":"sha256:d56d83713c2a6888c7e665edba1eb6ed9e303d471d031f75bb36a5c243ae12a0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/A5EP7OYTTAEM7UVBWGT2BGFGIR/bundle.json","state_url":"https://pith.science/pith/A5EP7OYTTAEM7UVBWGT2BGFGIR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/A5EP7OYTTAEM7UVBWGT2BGFGIR/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":"2026-08-04T15:41:32Z","links":{"resolver":"https://pith.science/pith/A5EP7OYTTAEM7UVBWGT2BGFGIR","bundle":"https://pith.science/pith/A5EP7OYTTAEM7UVBWGT2BGFGIR/bundle.json","state":"https://pith.science/pith/A5EP7OYTTAEM7UVBWGT2BGFGIR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/A5EP7OYTTAEM7UVBWGT2BGFGIR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:A5EP7OYTTAEM7UVBWGT2BGFGIR","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":"877b4309b0c661cf1715fdb48c0f3aad6c01013c50c76efbef1ee1fb92d1d08b","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-06-06T14:04:28Z","title_canon_sha256":"89f8e07b20181f718eef89753eb870d127599c16d517d96bd46f04ef9a7dfba3"},"schema_version":"1.0","source":{"id":"1806.02193","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.02193","created_at":"2026-07-05T00:49:33Z"},{"alias_kind":"arxiv_version","alias_value":"1806.02193v2","created_at":"2026-07-05T00:49:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.02193","created_at":"2026-07-05T00:49:33Z"},{"alias_kind":"pith_short_12","alias_value":"A5EP7OYTTAEM","created_at":"2026-07-05T00:49:33Z"},{"alias_kind":"pith_short_16","alias_value":"A5EP7OYTTAEM7UVB","created_at":"2026-07-05T00:49:33Z"},{"alias_kind":"pith_short_8","alias_value":"A5EP7OYT","created_at":"2026-07-05T00:49:33Z"}],"graph_snapshots":[{"event_id":"sha256:d56d83713c2a6888c7e665edba1eb6ed9e303d471d031f75bb36a5c243ae12a0","target":"graph","created_at":"2026-07-05T00:49:33Z","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/1806.02193/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The problem of accurately measuring the similarity between graphs is at the core of many applications in a variety of disciplines. Graph kernels have recently emerged as a promising approach to this problem. There are now many kernels, each focusing on different structural aspects of graphs. Here, we present GraKeL, a library that unifies several graph kernels into a common framework. The library is written in Python and adheres to the scikit-learn interface. It is simple to use and can be naturally combined with scikit-learn's modules to build a complete machine learning pipeline for tasks su","authors_text":"Christos Giatsidis, Giannis Nikolentzos, Giannis Siglidis, Konstantinos Skianis, Michalis Vazirgiannis, Stratis Limnios","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-06-06T14:04:28Z","title":"GraKeL: A Graph Kernel Library in Python"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.02193","kind":"arxiv","version":2},"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":"sha256:014480c64b1e1a166f23adb5124070fd42059ea4c80107dc37fd27c34a792d52","target":"record","created_at":"2026-07-05T00:49:33Z","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":"877b4309b0c661cf1715fdb48c0f3aad6c01013c50c76efbef1ee1fb92d1d08b","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-06-06T14:04:28Z","title_canon_sha256":"89f8e07b20181f718eef89753eb870d127599c16d517d96bd46f04ef9a7dfba3"},"schema_version":"1.0","source":{"id":"1806.02193","kind":"arxiv","version":2}},"canonical_sha256":"0748ffbb139808cfd2a1b1a7a098a6446a73a7b879ecc11f1186a4a63662845d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0748ffbb139808cfd2a1b1a7a098a6446a73a7b879ecc11f1186a4a63662845d","first_computed_at":"2026-07-05T00:49:33.908477Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:49:33.908477Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"QbO2QWA1sAcmdZJ64Sds/IYjABRZoOrttRP7GPtXJqoZQswp6djf0GRRAC5EzTC59KsoV3hKck49gRyrjC/GAA==","signature_status":"signed_v1","signed_at":"2026-07-05T00:49:33.909013Z","signed_message":"canonical_sha256_bytes"},"source_id":"1806.02193","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:014480c64b1e1a166f23adb5124070fd42059ea4c80107dc37fd27c34a792d52","sha256:d56d83713c2a6888c7e665edba1eb6ed9e303d471d031f75bb36a5c243ae12a0"],"state_sha256":"eea7a2a2801c807d833c1d94d1434ba60d3e63f8dfd5fe4480c1956a8f7cef68"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"b0XvwhZHcy7nLs0HK4WnA7CXwk3yO4CCI5tsBpRgKWal7FORo0iXl4y6VeIEzZ1oyd74COxghWfhmL15680AAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T15:41:32.072802Z","bundle_sha256":"39c1b862638d335264c6756b1dea01a9395fdf3a1fe604723ca8f3eb40ec773f"}}