{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:5IYUC66FYDXBA5AIHAMHIF2BNA","short_pith_number":"pith:5IYUC66F","schema_version":"1.0","canonical_sha256":"ea31417bc5c0ee1074083818741741682f220cdd751e286c0f6b678d972e29c5","source":{"kind":"arxiv","id":"1708.03921","version":1},"attestation_state":"computed","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,"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"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"},"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"},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1708.03921","created_at":"2026-05-18T00:38:07.503593+00:00"},{"alias_kind":"arxiv_version","alias_value":"1708.03921v1","created_at":"2026-05-18T00:38:07.503593+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.03921","created_at":"2026-05-18T00:38:07.503593+00:00"},{"alias_kind":"pith_short_12","alias_value":"5IYUC66FYDXB","created_at":"2026-05-18T12:31:00.734936+00:00"},{"alias_kind":"pith_short_16","alias_value":"5IYUC66FYDXBA5AI","created_at":"2026-05-18T12:31:00.734936+00:00"},{"alias_kind":"pith_short_8","alias_value":"5IYUC66F","created_at":"2026-05-18T12:31:00.734936+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5IYUC66FYDXBA5AIHAMHIF2BNA","json":"https://pith.science/pith/5IYUC66FYDXBA5AIHAMHIF2BNA.json","graph_json":"https://pith.science/api/pith-number/5IYUC66FYDXBA5AIHAMHIF2BNA/graph.json","events_json":"https://pith.science/api/pith-number/5IYUC66FYDXBA5AIHAMHIF2BNA/events.json","paper":"https://pith.science/paper/5IYUC66F"},"agent_actions":{"view_html":"https://pith.science/pith/5IYUC66FYDXBA5AIHAMHIF2BNA","download_json":"https://pith.science/pith/5IYUC66FYDXBA5AIHAMHIF2BNA.json","view_paper":"https://pith.science/paper/5IYUC66F","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1708.03921&json=true","fetch_graph":"https://pith.science/api/pith-number/5IYUC66FYDXBA5AIHAMHIF2BNA/graph.json","fetch_events":"https://pith.science/api/pith-number/5IYUC66FYDXBA5AIHAMHIF2BNA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5IYUC66FYDXBA5AIHAMHIF2BNA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5IYUC66FYDXBA5AIHAMHIF2BNA/action/storage_attestation","attest_author":"https://pith.science/pith/5IYUC66FYDXBA5AIHAMHIF2BNA/action/author_attestation","sign_citation":"https://pith.science/pith/5IYUC66FYDXBA5AIHAMHIF2BNA/action/citation_signature","submit_replication":"https://pith.science/pith/5IYUC66FYDXBA5AIHAMHIF2BNA/action/replication_record"}},"created_at":"2026-05-18T00:38:07.503593+00:00","updated_at":"2026-05-18T00:38:07.503593+00:00"}