{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:2F2BPGYI5456FFDFRSQAJ34MQI","short_pith_number":"pith:2F2BPGYI","canonical_record":{"source":{"id":"1803.00425","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-28T12:37:41Z","cross_cats_sorted":[],"title_canon_sha256":"b32b7362786b8dfa9253fb16934505a66a2893a1f8402f4821cbeb07c1109402","abstract_canon_sha256":"3fab0c31eddb6f2774bc56dd8b5b96f8ccead3143159f47383d1f4cf182e428a"},"schema_version":"1.0"},"canonical_sha256":"d174179b08ef3be294658ca004ef8c821dcfd9540591c49b268eb4bcca5217b3","source":{"kind":"arxiv","id":"1803.00425","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.00425","created_at":"2026-05-18T00:22:12Z"},{"alias_kind":"arxiv_version","alias_value":"1803.00425v1","created_at":"2026-05-18T00:22:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.00425","created_at":"2026-05-18T00:22:12Z"},{"alias_kind":"pith_short_12","alias_value":"2F2BPGYI5456","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_16","alias_value":"2F2BPGYI5456FFDF","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_8","alias_value":"2F2BPGYI","created_at":"2026-05-18T12:31:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:2F2BPGYI5456FFDFRSQAJ34MQI","target":"record","payload":{"canonical_record":{"source":{"id":"1803.00425","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-28T12:37:41Z","cross_cats_sorted":[],"title_canon_sha256":"b32b7362786b8dfa9253fb16934505a66a2893a1f8402f4821cbeb07c1109402","abstract_canon_sha256":"3fab0c31eddb6f2774bc56dd8b5b96f8ccead3143159f47383d1f4cf182e428a"},"schema_version":"1.0"},"canonical_sha256":"d174179b08ef3be294658ca004ef8c821dcfd9540591c49b268eb4bcca5217b3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:22:12.195054Z","signature_b64":"6ESHk/IzhHnrKmyM70LnsQhXN4DRKUO6Nn5Dl8m7UZZR7hVg26tDp8qMvRmMcjjSzRy2ClTTAoRV42vIBi1ZAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d174179b08ef3be294658ca004ef8c821dcfd9540591c49b268eb4bcca5217b3","last_reissued_at":"2026-05-18T00:22:12.194572Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:22:12.194572Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1803.00425","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":"2026-05-18T00:22:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Vn7rSpHSGtG27McHIFZ6lqjkyrT91J/chhAZQBA0jlAywOfJNiyZI8DCCrKuTiNXlXF4+Dmt0p7F8epaJpC/Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T08:21:34.930225Z"},"content_sha256":"80ce4b1d5ba8ad69a7659cbedfad7d9a934e85c21badc133261259fc2dfb43b1","schema_version":"1.0","event_id":"sha256:80ce4b1d5ba8ad69a7659cbedfad7d9a934e85c21badc133261259fc2dfb43b1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:2F2BPGYI5456FFDFRSQAJ34MQI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Graph Kernels based on High Order Graphlet Parsing and Hashing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anjan Dutta, Hichem Sahbi","submitted_at":"2018-02-28T12:37:41Z","abstract_excerpt":"Graph-based methods are known to be successful in many machine learning and pattern classification tasks. These methods consider semi-structured data as graphs where nodes correspond to primitives (parts, interest points, segments, etc.) and edges characterize the relationships between these primitives. However, these non-vectorial graph data cannot be straightforwardly plugged into off-the-shelf machine learning algorithms without a preliminary step of -- explicit/implicit -- graph vectorization and embedding. This embedding process should be resilient to intra-class graph variations while be"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.00425","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":""},"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":"2026-05-18T00:22:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TnNInK08D7t9iWo/SDznXrHNQMy+cuQ9VXMudrbujSMiJTmg60z22CPR5y8oiNxZYJgxBIe5zS8VfVDtcji1Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T08:21:34.930976Z"},"content_sha256":"1e762b92774dcdbfb38f0ce01fada30a90d41c6eed68c00ebc16a3f790d1ba47","schema_version":"1.0","event_id":"sha256:1e762b92774dcdbfb38f0ce01fada30a90d41c6eed68c00ebc16a3f790d1ba47"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2F2BPGYI5456FFDFRSQAJ34MQI/bundle.json","state_url":"https://pith.science/pith/2F2BPGYI5456FFDFRSQAJ34MQI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2F2BPGYI5456FFDFRSQAJ34MQI/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":"2026-06-06T08:21:34Z","links":{"resolver":"https://pith.science/pith/2F2BPGYI5456FFDFRSQAJ34MQI","bundle":"https://pith.science/pith/2F2BPGYI5456FFDFRSQAJ34MQI/bundle.json","state":"https://pith.science/pith/2F2BPGYI5456FFDFRSQAJ34MQI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2F2BPGYI5456FFDFRSQAJ34MQI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:2F2BPGYI5456FFDFRSQAJ34MQI","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":"3fab0c31eddb6f2774bc56dd8b5b96f8ccead3143159f47383d1f4cf182e428a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-28T12:37:41Z","title_canon_sha256":"b32b7362786b8dfa9253fb16934505a66a2893a1f8402f4821cbeb07c1109402"},"schema_version":"1.0","source":{"id":"1803.00425","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.00425","created_at":"2026-05-18T00:22:12Z"},{"alias_kind":"arxiv_version","alias_value":"1803.00425v1","created_at":"2026-05-18T00:22:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.00425","created_at":"2026-05-18T00:22:12Z"},{"alias_kind":"pith_short_12","alias_value":"2F2BPGYI5456","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_16","alias_value":"2F2BPGYI5456FFDF","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_8","alias_value":"2F2BPGYI","created_at":"2026-05-18T12:31:59Z"}],"graph_snapshots":[{"event_id":"sha256:1e762b92774dcdbfb38f0ce01fada30a90d41c6eed68c00ebc16a3f790d1ba47","target":"graph","created_at":"2026-05-18T00:22:12Z","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"},"paper":{"abstract_excerpt":"Graph-based methods are known to be successful in many machine learning and pattern classification tasks. These methods consider semi-structured data as graphs where nodes correspond to primitives (parts, interest points, segments, etc.) and edges characterize the relationships between these primitives. However, these non-vectorial graph data cannot be straightforwardly plugged into off-the-shelf machine learning algorithms without a preliminary step of -- explicit/implicit -- graph vectorization and embedding. This embedding process should be resilient to intra-class graph variations while be","authors_text":"Anjan Dutta, Hichem Sahbi","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-28T12:37:41Z","title":"Graph Kernels based on High Order Graphlet Parsing and Hashing"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.00425","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":"sha256:80ce4b1d5ba8ad69a7659cbedfad7d9a934e85c21badc133261259fc2dfb43b1","target":"record","created_at":"2026-05-18T00:22:12Z","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":"3fab0c31eddb6f2774bc56dd8b5b96f8ccead3143159f47383d1f4cf182e428a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-28T12:37:41Z","title_canon_sha256":"b32b7362786b8dfa9253fb16934505a66a2893a1f8402f4821cbeb07c1109402"},"schema_version":"1.0","source":{"id":"1803.00425","kind":"arxiv","version":1}},"canonical_sha256":"d174179b08ef3be294658ca004ef8c821dcfd9540591c49b268eb4bcca5217b3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d174179b08ef3be294658ca004ef8c821dcfd9540591c49b268eb4bcca5217b3","first_computed_at":"2026-05-18T00:22:12.194572Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:22:12.194572Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6ESHk/IzhHnrKmyM70LnsQhXN4DRKUO6Nn5Dl8m7UZZR7hVg26tDp8qMvRmMcjjSzRy2ClTTAoRV42vIBi1ZAQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:22:12.195054Z","signed_message":"canonical_sha256_bytes"},"source_id":"1803.00425","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:80ce4b1d5ba8ad69a7659cbedfad7d9a934e85c21badc133261259fc2dfb43b1","sha256:1e762b92774dcdbfb38f0ce01fada30a90d41c6eed68c00ebc16a3f790d1ba47"],"state_sha256":"f1ac1af2afa12ec36bfb45490a5def849d854e745ba8ac1e023aff364dc37008"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cRi98/BqsQT7EZGgLHfAknuEbXqW6MnlP9mF7iXdDxuooAuYUKYicrNx9emur69No1uFQ6Jos80oHNB9EBAoBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-06T08:21:34.937498Z","bundle_sha256":"5be7de97099429efea267efcb90ebce14f300a0813fd82ca4c389c4896a0220c"}}