{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:ACXOOBTMURQIHDZWJODLVL65G5","short_pith_number":"pith:ACXOOBTM","schema_version":"1.0","canonical_sha256":"00aee7066ca460838f364b86baafdd377d43c0b8ed27ba0cfc8aebafdfa6a34c","source":{"kind":"arxiv","id":"1811.02662","version":5},"attestation_state":"computed","paper":{"title":"Similarity Learning with Higher-Order Graph Convolutions for Brain Network Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Dipanjan Sengupta, Guixiang Ma, Michael W. Cole, Nesreen K. Ahmed, Nicholas B. Turk-Browne, Philip S. Yu, Ted Willke","submitted_at":"2018-11-02T03:51:45Z","abstract_excerpt":"Learning a similarity metric has gained much attention recently, where the goal is to learn a function that maps input patterns to a target space while preserving the semantic distance in the input space. While most related work focused on images, we focus instead on learning a similarity metric for neuroimages, such as fMRI and DTI images. We propose an end-to-end similarity learning framework called Higher-order Siamese GCN for multi-subject fMRI data analysis. The proposed framework learns the brain network representations via a supervised metric-based approach with siamese neural networks "},"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":"1811.02662","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-02T03:51:45Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"20cdaeb2c610dfc8e15f488cae8035ae47b343cee71b62c416dfa0e9116740ea","abstract_canon_sha256":"53ecd96a6b061dcb155ff30c009ab5e0677ec7224726acf38493227c296e65be"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:47:12.361358Z","signature_b64":"6xpvAKO6d60Vp/s1Oo+RK8n5GwkxgTnB8p2FwrWq8U2ddYdDZKJNUBUmGYPoGMfLfSRit5nlcaDssNBfNw20Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"00aee7066ca460838f364b86baafdd377d43c0b8ed27ba0cfc8aebafdfa6a34c","last_reissued_at":"2026-05-17T23:47:12.360891Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:47:12.360891Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Similarity Learning with Higher-Order Graph Convolutions for Brain Network Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Dipanjan Sengupta, Guixiang Ma, Michael W. Cole, Nesreen K. Ahmed, Nicholas B. Turk-Browne, Philip S. Yu, Ted Willke","submitted_at":"2018-11-02T03:51:45Z","abstract_excerpt":"Learning a similarity metric has gained much attention recently, where the goal is to learn a function that maps input patterns to a target space while preserving the semantic distance in the input space. While most related work focused on images, we focus instead on learning a similarity metric for neuroimages, such as fMRI and DTI images. We propose an end-to-end similarity learning framework called Higher-order Siamese GCN for multi-subject fMRI data analysis. The proposed framework learns the brain network representations via a supervised metric-based approach with siamese neural networks "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.02662","kind":"arxiv","version":5},"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":"1811.02662","created_at":"2026-05-17T23:47:12.360962+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.02662v5","created_at":"2026-05-17T23:47:12.360962+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.02662","created_at":"2026-05-17T23:47:12.360962+00:00"},{"alias_kind":"pith_short_12","alias_value":"ACXOOBTMURQI","created_at":"2026-05-18T12:32:13.499390+00:00"},{"alias_kind":"pith_short_16","alias_value":"ACXOOBTMURQIHDZW","created_at":"2026-05-18T12:32:13.499390+00:00"},{"alias_kind":"pith_short_8","alias_value":"ACXOOBTM","created_at":"2026-05-18T12:32:13.499390+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/ACXOOBTMURQIHDZWJODLVL65G5","json":"https://pith.science/pith/ACXOOBTMURQIHDZWJODLVL65G5.json","graph_json":"https://pith.science/api/pith-number/ACXOOBTMURQIHDZWJODLVL65G5/graph.json","events_json":"https://pith.science/api/pith-number/ACXOOBTMURQIHDZWJODLVL65G5/events.json","paper":"https://pith.science/paper/ACXOOBTM"},"agent_actions":{"view_html":"https://pith.science/pith/ACXOOBTMURQIHDZWJODLVL65G5","download_json":"https://pith.science/pith/ACXOOBTMURQIHDZWJODLVL65G5.json","view_paper":"https://pith.science/paper/ACXOOBTM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.02662&json=true","fetch_graph":"https://pith.science/api/pith-number/ACXOOBTMURQIHDZWJODLVL65G5/graph.json","fetch_events":"https://pith.science/api/pith-number/ACXOOBTMURQIHDZWJODLVL65G5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ACXOOBTMURQIHDZWJODLVL65G5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ACXOOBTMURQIHDZWJODLVL65G5/action/storage_attestation","attest_author":"https://pith.science/pith/ACXOOBTMURQIHDZWJODLVL65G5/action/author_attestation","sign_citation":"https://pith.science/pith/ACXOOBTMURQIHDZWJODLVL65G5/action/citation_signature","submit_replication":"https://pith.science/pith/ACXOOBTMURQIHDZWJODLVL65G5/action/replication_record"}},"created_at":"2026-05-17T23:47:12.360962+00:00","updated_at":"2026-05-17T23:47:12.360962+00:00"}