{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:RRHW756RZJNHX5NSPKXFKLEAWT","short_pith_number":"pith:RRHW756R","schema_version":"1.0","canonical_sha256":"8c4f6ff7d1ca5a7bf5b27aae552c80b4cfa645adde9fa6d2e3f12abeca30baff","source":{"kind":"arxiv","id":"2405.13806","version":2},"attestation_state":"computed","paper":{"title":"A General Graph Spectral Wavelet Convolution via Chebyshev Order Decomposition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Francesco Di Giovanni, Michael M. Bronstein, Nian Liu, Thomas Laurent, Xavier Bresson, Xiaoxin He","submitted_at":"2024-05-22T16:32:27Z","abstract_excerpt":"Spectral graph convolution, an important tool of data filtering on graphs, relies on two essential decisions: selecting spectral bases for signal transformation and parameterizing the kernel for frequency analysis. While recent techniques mainly focus on standard Fourier transform and vector-valued spectral functions, they fall short in flexibility to model signal distributions over large spatial ranges, and capacity of spectral function. In this paper, we present a novel wavelet-based graph convolution network, namely WaveGC, which integrates multi-resolution spectral bases and a matrix-value"},"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":"2405.13806","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-05-22T16:32:27Z","cross_cats_sorted":[],"title_canon_sha256":"bbaf40650b54fbc0abc360f185fbf2167cf2b5b03fde1eb29121c8575f457256","abstract_canon_sha256":"ca46af6f0146cfbc1032f92b4e543390704139ae639193cd1079bdfd639f2340"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:03:00.494821Z","signature_b64":"W2lPL91xmx+whk2oJTWFzNXQpzDqy4EKQweaJhnrW3Y9cMXoJxoQ0xYtIxcFelhuCkqinNow2KpYZEnWxqfnCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8c4f6ff7d1ca5a7bf5b27aae552c80b4cfa645adde9fa6d2e3f12abeca30baff","last_reissued_at":"2026-07-05T11:03:00.494291Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:03:00.494291Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A General Graph Spectral Wavelet Convolution via Chebyshev Order Decomposition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Francesco Di Giovanni, Michael M. Bronstein, Nian Liu, Thomas Laurent, Xavier Bresson, Xiaoxin He","submitted_at":"2024-05-22T16:32:27Z","abstract_excerpt":"Spectral graph convolution, an important tool of data filtering on graphs, relies on two essential decisions: selecting spectral bases for signal transformation and parameterizing the kernel for frequency analysis. While recent techniques mainly focus on standard Fourier transform and vector-valued spectral functions, they fall short in flexibility to model signal distributions over large spatial ranges, and capacity of spectral function. In this paper, we present a novel wavelet-based graph convolution network, namely WaveGC, which integrates multi-resolution spectral bases and a matrix-value"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.13806","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/2405.13806/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":"2405.13806","created_at":"2026-07-05T11:03:00.494369+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.13806v2","created_at":"2026-07-05T11:03:00.494369+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.13806","created_at":"2026-07-05T11:03:00.494369+00:00"},{"alias_kind":"pith_short_12","alias_value":"RRHW756RZJNH","created_at":"2026-07-05T11:03:00.494369+00:00"},{"alias_kind":"pith_short_16","alias_value":"RRHW756RZJNHX5NS","created_at":"2026-07-05T11:03:00.494369+00:00"},{"alias_kind":"pith_short_8","alias_value":"RRHW756R","created_at":"2026-07-05T11:03:00.494369+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/RRHW756RZJNHX5NSPKXFKLEAWT","json":"https://pith.science/pith/RRHW756RZJNHX5NSPKXFKLEAWT.json","graph_json":"https://pith.science/api/pith-number/RRHW756RZJNHX5NSPKXFKLEAWT/graph.json","events_json":"https://pith.science/api/pith-number/RRHW756RZJNHX5NSPKXFKLEAWT/events.json","paper":"https://pith.science/paper/RRHW756R"},"agent_actions":{"view_html":"https://pith.science/pith/RRHW756RZJNHX5NSPKXFKLEAWT","download_json":"https://pith.science/pith/RRHW756RZJNHX5NSPKXFKLEAWT.json","view_paper":"https://pith.science/paper/RRHW756R","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.13806&json=true","fetch_graph":"https://pith.science/api/pith-number/RRHW756RZJNHX5NSPKXFKLEAWT/graph.json","fetch_events":"https://pith.science/api/pith-number/RRHW756RZJNHX5NSPKXFKLEAWT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RRHW756RZJNHX5NSPKXFKLEAWT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RRHW756RZJNHX5NSPKXFKLEAWT/action/storage_attestation","attest_author":"https://pith.science/pith/RRHW756RZJNHX5NSPKXFKLEAWT/action/author_attestation","sign_citation":"https://pith.science/pith/RRHW756RZJNHX5NSPKXFKLEAWT/action/citation_signature","submit_replication":"https://pith.science/pith/RRHW756RZJNHX5NSPKXFKLEAWT/action/replication_record"}},"created_at":"2026-07-05T11:03:00.494369+00:00","updated_at":"2026-07-05T11:03:00.494369+00:00"}