{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:5IYUC66FYDXBA5AIHAMHIF2BNA","short_pith_number":"pith:5IYUC66F","canonical_record":{"source":{"id":"1708.03921","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-13T15:21:22Z","cross_cats_sorted":[],"title_canon_sha256":"54da2285b195933f900d3286497eab43967c2d1fca820eeb66c8dd729c5a8b26","abstract_canon_sha256":"65c84e1b47db3ac5775726559727e1c540d666c9d06b81ced3a50af8d7d97bb8"},"schema_version":"1.0"},"canonical_sha256":"ea31417bc5c0ee1074083818741741682f220cdd751e286c0f6b678d972e29c5","source":{"kind":"arxiv","id":"1708.03921","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.03921","created_at":"2026-05-18T00:38:07Z"},{"alias_kind":"arxiv_version","alias_value":"1708.03921v1","created_at":"2026-05-18T00:38:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.03921","created_at":"2026-05-18T00:38:07Z"},{"alias_kind":"pith_short_12","alias_value":"5IYUC66FYDXB","created_at":"2026-05-18T12:31:00Z"},{"alias_kind":"pith_short_16","alias_value":"5IYUC66FYDXBA5AI","created_at":"2026-05-18T12:31:00Z"},{"alias_kind":"pith_short_8","alias_value":"5IYUC66F","created_at":"2026-05-18T12:31:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:5IYUC66FYDXBA5AIHAMHIF2BNA","target":"record","payload":{"canonical_record":{"source":{"id":"1708.03921","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-13T15:21:22Z","cross_cats_sorted":[],"title_canon_sha256":"54da2285b195933f900d3286497eab43967c2d1fca820eeb66c8dd729c5a8b26","abstract_canon_sha256":"65c84e1b47db3ac5775726559727e1c540d666c9d06b81ced3a50af8d7d97bb8"},"schema_version":"1.0"},"canonical_sha256":"ea31417bc5c0ee1074083818741741682f220cdd751e286c0f6b678d972e29c5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:38:07.503977Z","signature_b64":"PTr1lZTwvYiEjFv5PW4PhnsbqmIqPkP2VJmfZBEe0N5V/WS472USgZnDXOjuXpwkgKNNT5e/bCFTlnXFjCagBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ea31417bc5c0ee1074083818741741682f220cdd751e286c0f6b678d972e29c5","last_reissued_at":"2026-05-18T00:38:07.503531Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:38:07.503531Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1708.03921","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":"2026-05-18T00:38:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6GTHECpxx0tzt1EkdDN/KBQqqd9iR8dhfNIF/M+LKmTCrKv3Es8bQZ5oL+few1E/h7U41QvjJYCp4c+aDocPAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T10:19:38.689251Z"},"content_sha256":"af73fc42213a35b2287b0cc632f900885d88e5bb03b63a2bda2fcb87a00fe9cc","schema_version":"1.0","event_id":"sha256:af73fc42213a35b2287b0cc632f900885d88e5bb03b63a2bda2fcb87a00fe9cc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:5IYUC66FYDXBA5AIHAMHIF2BNA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Visual Graph Mining","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Quanshi Zhang, Ryosuke Shibasaki, Xuan Song","submitted_at":"2017-08-13T15:21:22Z","abstract_excerpt":"In this study, we formulate the concept of \"mining maximal-size frequent subgraphs\" in the challenging domain of visual data (images and videos). In general, visual knowledge can usually be modeled as attributed relational graphs (ARGs) with local attributes representing local parts and pairwise attributes describing the spatial relationship between parts. Thus, from a practical perspective, such mining of maximal-size subgraphs can be regarded as a general platform for discovering and modeling the common objects within cluttered and unlabeled visual data. Then, from a theoretical perspective,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.03921","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":"2026-05-18T00:38:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ucovmIj5NC2qcra+1ck49JBbCUhGcjw5ykyS1NELqleywiHH/jaMVE2pAWFFuqZJk1myGIJe6md4qbs5CylbDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T10:19:38.689618Z"},"content_sha256":"fbafd103384ecb2f886542373a3b63cb82d6bd991ca452d30bf5ea7c6bbf4e9b","schema_version":"1.0","event_id":"sha256:fbafd103384ecb2f886542373a3b63cb82d6bd991ca452d30bf5ea7c6bbf4e9b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5IYUC66FYDXBA5AIHAMHIF2BNA/bundle.json","state_url":"https://pith.science/pith/5IYUC66FYDXBA5AIHAMHIF2BNA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5IYUC66FYDXBA5AIHAMHIF2BNA/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":"2026-06-05T10:19:38Z","links":{"resolver":"https://pith.science/pith/5IYUC66FYDXBA5AIHAMHIF2BNA","bundle":"https://pith.science/pith/5IYUC66FYDXBA5AIHAMHIF2BNA/bundle.json","state":"https://pith.science/pith/5IYUC66FYDXBA5AIHAMHIF2BNA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5IYUC66FYDXBA5AIHAMHIF2BNA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:5IYUC66FYDXBA5AIHAMHIF2BNA","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":"65c84e1b47db3ac5775726559727e1c540d666c9d06b81ced3a50af8d7d97bb8","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-13T15:21:22Z","title_canon_sha256":"54da2285b195933f900d3286497eab43967c2d1fca820eeb66c8dd729c5a8b26"},"schema_version":"1.0","source":{"id":"1708.03921","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.03921","created_at":"2026-05-18T00:38:07Z"},{"alias_kind":"arxiv_version","alias_value":"1708.03921v1","created_at":"2026-05-18T00:38:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.03921","created_at":"2026-05-18T00:38:07Z"},{"alias_kind":"pith_short_12","alias_value":"5IYUC66FYDXB","created_at":"2026-05-18T12:31:00Z"},{"alias_kind":"pith_short_16","alias_value":"5IYUC66FYDXBA5AI","created_at":"2026-05-18T12:31:00Z"},{"alias_kind":"pith_short_8","alias_value":"5IYUC66F","created_at":"2026-05-18T12:31:00Z"}],"graph_snapshots":[{"event_id":"sha256:fbafd103384ecb2f886542373a3b63cb82d6bd991ca452d30bf5ea7c6bbf4e9b","target":"graph","created_at":"2026-05-18T00:38:07Z","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":"In this study, we formulate the concept of \"mining maximal-size frequent subgraphs\" in the challenging domain of visual data (images and videos). In general, visual knowledge can usually be modeled as attributed relational graphs (ARGs) with local attributes representing local parts and pairwise attributes describing the spatial relationship between parts. Thus, from a practical perspective, such mining of maximal-size subgraphs can be regarded as a general platform for discovering and modeling the common objects within cluttered and unlabeled visual data. Then, from a theoretical perspective,","authors_text":"Quanshi Zhang, Ryosuke Shibasaki, Xuan Song","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-13T15:21:22Z","title":"Visual Graph Mining"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.03921","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:af73fc42213a35b2287b0cc632f900885d88e5bb03b63a2bda2fcb87a00fe9cc","target":"record","created_at":"2026-05-18T00:38:07Z","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":"65c84e1b47db3ac5775726559727e1c540d666c9d06b81ced3a50af8d7d97bb8","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-13T15:21:22Z","title_canon_sha256":"54da2285b195933f900d3286497eab43967c2d1fca820eeb66c8dd729c5a8b26"},"schema_version":"1.0","source":{"id":"1708.03921","kind":"arxiv","version":1}},"canonical_sha256":"ea31417bc5c0ee1074083818741741682f220cdd751e286c0f6b678d972e29c5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ea31417bc5c0ee1074083818741741682f220cdd751e286c0f6b678d972e29c5","first_computed_at":"2026-05-18T00:38:07.503531Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:38:07.503531Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PTr1lZTwvYiEjFv5PW4PhnsbqmIqPkP2VJmfZBEe0N5V/WS472USgZnDXOjuXpwkgKNNT5e/bCFTlnXFjCagBQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:38:07.503977Z","signed_message":"canonical_sha256_bytes"},"source_id":"1708.03921","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:af73fc42213a35b2287b0cc632f900885d88e5bb03b63a2bda2fcb87a00fe9cc","sha256:fbafd103384ecb2f886542373a3b63cb82d6bd991ca452d30bf5ea7c6bbf4e9b"],"state_sha256":"e822942b6c6efc19ecb3d8e7d3280338e7e7c1b4f1ad3a2f5cd257460a9fdd08"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wJd/gyYXHtCTJjKYFB1desxfofygPR9KN/4x/upAERzvMeQrRG4EJaPLxYGSu250zurbWnZs2aT15GHsk32vBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-05T10:19:38.691460Z","bundle_sha256":"98e8e9a7ed7f2a4c67b11f3d7c68e0402757b52f233c789152ce820c6b9f813d"}}