{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:RYZI4TKCVOGW7TLSGT7DZ47VNR","short_pith_number":"pith:RYZI4TKC","schema_version":"1.0","canonical_sha256":"8e328e4d42ab8d6fcd7234fe3cf3f56c4c1eda95f982a2fe94f57bdf481db6d3","source":{"kind":"arxiv","id":"2511.16111","version":2},"attestation_state":"computed","paper":{"title":"Rotation-Parameterized Graph Fractional Fourier Transform: Definition, Properties, and Optimal Filtering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.SP"],"primary_cat":"stat.ML","authors_text":"Feiyue Zhao, Mingzhi Wang, Yangfan He, Zhichao Zhang","submitted_at":"2025-11-20T07:13:27Z","abstract_excerpt":"Graph spectral representations are fundamental in graph signal processing, providing a rigorous frameworkforanalyzing graph-structured data. The graph fractional Fourier transform (GFRFT) extends the graph Fourier transform (GFT) through a fractional-order parameter, enabling flexible spectral analysis with mathematical consistency. The angular graph Fourier transform (AGFT) further introduces angular control by rotating GFT eigenvectors; however, existing constructions may fail to reduce exactly to the GFT at zero angle, weakening theoretical consistency and interpretability. To address these"},"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":"2511.16111","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2025-11-20T07:13:27Z","cross_cats_sorted":["cs.LG","math.SP"],"title_canon_sha256":"d6cc9264c036fd7a1e6f41e3208ae1391a6667a7c5bb264b3f7561a4847d8e1a","abstract_canon_sha256":"f17d0e836c228cb13b81ca1459dd30cbdf71d79f15ec3159fa435c7d704d752f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-05T01:14:31.011345Z","signature_b64":"hFXzoq0FiVrmA11kOMGS4Ysbhycj+3a3OD17JCE1jV9nlqpQghR6hpWdk3YNKlVUa3n+0V4vOuMIXSBmbfNcBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8e328e4d42ab8d6fcd7234fe3cf3f56c4c1eda95f982a2fe94f57bdf481db6d3","last_reissued_at":"2026-06-05T01:14:31.010868Z","signature_status":"signed_v1","first_computed_at":"2026-06-05T01:14:31.010868Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Rotation-Parameterized Graph Fractional Fourier Transform: Definition, Properties, and Optimal Filtering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.SP"],"primary_cat":"stat.ML","authors_text":"Feiyue Zhao, Mingzhi Wang, Yangfan He, Zhichao Zhang","submitted_at":"2025-11-20T07:13:27Z","abstract_excerpt":"Graph spectral representations are fundamental in graph signal processing, providing a rigorous frameworkforanalyzing graph-structured data. The graph fractional Fourier transform (GFRFT) extends the graph Fourier transform (GFT) through a fractional-order parameter, enabling flexible spectral analysis with mathematical consistency. The angular graph Fourier transform (AGFT) further introduces angular control by rotating GFT eigenvectors; however, existing constructions may fail to reduce exactly to the GFT at zero angle, weakening theoretical consistency and interpretability. To address these"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2511.16111","kind":"arxiv","version":2},"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/2511.16111/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":"2511.16111","created_at":"2026-06-05T01:14:31.010922+00:00"},{"alias_kind":"arxiv_version","alias_value":"2511.16111v2","created_at":"2026-06-05T01:14:31.010922+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2511.16111","created_at":"2026-06-05T01:14:31.010922+00:00"},{"alias_kind":"pith_short_12","alias_value":"RYZI4TKCVOGW","created_at":"2026-06-05T01:14:31.010922+00:00"},{"alias_kind":"pith_short_16","alias_value":"RYZI4TKCVOGW7TLS","created_at":"2026-06-05T01:14:31.010922+00:00"},{"alias_kind":"pith_short_8","alias_value":"RYZI4TKC","created_at":"2026-06-05T01:14:31.010922+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2602.20870","citing_title":"FGFRFT: Fast Graph Fractional Fourier Transform via Exact Spectral Splitting and Fourier-Series Approximation","ref_index":27,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RYZI4TKCVOGW7TLSGT7DZ47VNR","json":"https://pith.science/pith/RYZI4TKCVOGW7TLSGT7DZ47VNR.json","graph_json":"https://pith.science/api/pith-number/RYZI4TKCVOGW7TLSGT7DZ47VNR/graph.json","events_json":"https://pith.science/api/pith-number/RYZI4TKCVOGW7TLSGT7DZ47VNR/events.json","paper":"https://pith.science/paper/RYZI4TKC"},"agent_actions":{"view_html":"https://pith.science/pith/RYZI4TKCVOGW7TLSGT7DZ47VNR","download_json":"https://pith.science/pith/RYZI4TKCVOGW7TLSGT7DZ47VNR.json","view_paper":"https://pith.science/paper/RYZI4TKC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2511.16111&json=true","fetch_graph":"https://pith.science/api/pith-number/RYZI4TKCVOGW7TLSGT7DZ47VNR/graph.json","fetch_events":"https://pith.science/api/pith-number/RYZI4TKCVOGW7TLSGT7DZ47VNR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RYZI4TKCVOGW7TLSGT7DZ47VNR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RYZI4TKCVOGW7TLSGT7DZ47VNR/action/storage_attestation","attest_author":"https://pith.science/pith/RYZI4TKCVOGW7TLSGT7DZ47VNR/action/author_attestation","sign_citation":"https://pith.science/pith/RYZI4TKCVOGW7TLSGT7DZ47VNR/action/citation_signature","submit_replication":"https://pith.science/pith/RYZI4TKCVOGW7TLSGT7DZ47VNR/action/replication_record"}},"created_at":"2026-06-05T01:14:31.010922+00:00","updated_at":"2026-06-05T01:14:31.010922+00:00"}