{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:UVKHASPEQL3LEIAJISXHK7JM6S","short_pith_number":"pith:UVKHASPE","schema_version":"1.0","canonical_sha256":"a5547049e482f6b2200944ae757d2cf4877e67fb05d4cdfd7f67f768f1a7309a","source":{"kind":"arxiv","id":"2404.14822","version":1},"attestation_state":"computed","paper":{"title":"CNN2GNN: How to Bridge CNN with GNN","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Hongyuan Zhang, Xuelong Li, Ziheng Jiao","submitted_at":"2024-04-23T08:19:08Z","abstract_excerpt":"Although the convolutional neural network (CNN) has achieved excellent performance in vision tasks by extracting the intra-sample representation, it will take a higher training expense because of stacking numerous convolutional layers. Recently, as the bilinear models, graph neural networks (GNN) have succeeded in exploring the underlying topological relationship among the graph data with a few graph neural layers. Unfortunately, it cannot be directly utilized on non-graph data due to the lack of graph structure and has high inference latency on large-scale scenarios. Inspired by these complem"},"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":"2404.14822","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-04-23T08:19:08Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"25fbd4e86684486dc294bc363be1b06f2e5bc9a310826cdf087a9d2c214c4e9d","abstract_canon_sha256":"a9eeaad03d8bf57c9f6213dba1d21bedeb0c66b7a3d5942524d1a43cfa35d24a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:11:10.583087Z","signature_b64":"AFkOtTRzGEq9PzcoEJx00nhmYsQgovjmnbboYUEhsa0a8EX/4yPx25FzQK2UsMvDAMS7QCs8oZQ0ycq82bcgCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a5547049e482f6b2200944ae757d2cf4877e67fb05d4cdfd7f67f768f1a7309a","last_reissued_at":"2026-07-05T08:11:10.582733Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:11:10.582733Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CNN2GNN: How to Bridge CNN with GNN","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Hongyuan Zhang, Xuelong Li, Ziheng Jiao","submitted_at":"2024-04-23T08:19:08Z","abstract_excerpt":"Although the convolutional neural network (CNN) has achieved excellent performance in vision tasks by extracting the intra-sample representation, it will take a higher training expense because of stacking numerous convolutional layers. Recently, as the bilinear models, graph neural networks (GNN) have succeeded in exploring the underlying topological relationship among the graph data with a few graph neural layers. Unfortunately, it cannot be directly utilized on non-graph data due to the lack of graph structure and has high inference latency on large-scale scenarios. Inspired by these complem"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.14822","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/2404.14822/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2404.14822","created_at":"2026-07-05T08:11:10.582788+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.14822v1","created_at":"2026-07-05T08:11:10.582788+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.14822","created_at":"2026-07-05T08:11:10.582788+00:00"},{"alias_kind":"pith_short_12","alias_value":"UVKHASPEQL3L","created_at":"2026-07-05T08:11:10.582788+00:00"},{"alias_kind":"pith_short_16","alias_value":"UVKHASPEQL3LEIAJ","created_at":"2026-07-05T08:11:10.582788+00:00"},{"alias_kind":"pith_short_8","alias_value":"UVKHASPE","created_at":"2026-07-05T08:11:10.582788+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/UVKHASPEQL3LEIAJISXHK7JM6S","json":"https://pith.science/pith/UVKHASPEQL3LEIAJISXHK7JM6S.json","graph_json":"https://pith.science/api/pith-number/UVKHASPEQL3LEIAJISXHK7JM6S/graph.json","events_json":"https://pith.science/api/pith-number/UVKHASPEQL3LEIAJISXHK7JM6S/events.json","paper":"https://pith.science/paper/UVKHASPE"},"agent_actions":{"view_html":"https://pith.science/pith/UVKHASPEQL3LEIAJISXHK7JM6S","download_json":"https://pith.science/pith/UVKHASPEQL3LEIAJISXHK7JM6S.json","view_paper":"https://pith.science/paper/UVKHASPE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.14822&json=true","fetch_graph":"https://pith.science/api/pith-number/UVKHASPEQL3LEIAJISXHK7JM6S/graph.json","fetch_events":"https://pith.science/api/pith-number/UVKHASPEQL3LEIAJISXHK7JM6S/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UVKHASPEQL3LEIAJISXHK7JM6S/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UVKHASPEQL3LEIAJISXHK7JM6S/action/storage_attestation","attest_author":"https://pith.science/pith/UVKHASPEQL3LEIAJISXHK7JM6S/action/author_attestation","sign_citation":"https://pith.science/pith/UVKHASPEQL3LEIAJISXHK7JM6S/action/citation_signature","submit_replication":"https://pith.science/pith/UVKHASPEQL3LEIAJISXHK7JM6S/action/replication_record"}},"created_at":"2026-07-05T08:11:10.582788+00:00","updated_at":"2026-07-05T08:11:10.582788+00:00"}