{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:RRHW756RZJNHX5NSPKXFKLEAWT","short_pith_number":"pith:RRHW756R","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"},"canonical_sha256":"8c4f6ff7d1ca5a7bf5b27aae552c80b4cfa645adde9fa6d2e3f12abeca30baff","source":{"kind":"arxiv","id":"2405.13806","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.13806","created_at":"2026-07-05T11:03:00Z"},{"alias_kind":"arxiv_version","alias_value":"2405.13806v2","created_at":"2026-07-05T11:03:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.13806","created_at":"2026-07-05T11:03:00Z"},{"alias_kind":"pith_short_12","alias_value":"RRHW756RZJNH","created_at":"2026-07-05T11:03:00Z"},{"alias_kind":"pith_short_16","alias_value":"RRHW756RZJNHX5NS","created_at":"2026-07-05T11:03:00Z"},{"alias_kind":"pith_short_8","alias_value":"RRHW756R","created_at":"2026-07-05T11:03:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:RRHW756RZJNHX5NSPKXFKLEAWT","target":"record","payload":{"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"},"canonical_sha256":"8c4f6ff7d1ca5a7bf5b27aae552c80b4cfa645adde9fa6d2e3f12abeca30baff","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"},"source_kind":"arxiv","source_id":"2405.13806","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:03:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wagXow4eO7ZW87e4qkR2I0sdIenS3laxz1MH2vgucexCN0tKCIGx1wzcO+jnfDvtP+VqM4E65/Aqf3i+P+NFAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T06:10:05.744430Z"},"content_sha256":"f0bc71f7b890dab8901783ae2f7913f457d9fa0f5395f03117376cbd45a6c0ef","schema_version":"1.0","event_id":"sha256:f0bc71f7b890dab8901783ae2f7913f457d9fa0f5395f03117376cbd45a6c0ef"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:RRHW756RZJNHX5NSPKXFKLEAWT","target":"graph","payload":{"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:03:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"M7R/jQjn1xjuGak+b0cHVbg/3RJjS1H4P+PP3B63NhFvf5NCNFMqcK6Q0xKl/KyQxY9dqAMvnKlDu0nyxFSGCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T06:10:05.744931Z"},"content_sha256":"57963a7375b9549b10a6ad6c5a343ea96e4f0d9fc314a1591e79f6eb102a68c5","schema_version":"1.0","event_id":"sha256:57963a7375b9549b10a6ad6c5a343ea96e4f0d9fc314a1591e79f6eb102a68c5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RRHW756RZJNHX5NSPKXFKLEAWT/bundle.json","state_url":"https://pith.science/pith/RRHW756RZJNHX5NSPKXFKLEAWT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RRHW756RZJNHX5NSPKXFKLEAWT/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-07T06:10:05Z","links":{"resolver":"https://pith.science/pith/RRHW756RZJNHX5NSPKXFKLEAWT","bundle":"https://pith.science/pith/RRHW756RZJNHX5NSPKXFKLEAWT/bundle.json","state":"https://pith.science/pith/RRHW756RZJNHX5NSPKXFKLEAWT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RRHW756RZJNHX5NSPKXFKLEAWT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:RRHW756RZJNHX5NSPKXFKLEAWT","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"ca46af6f0146cfbc1032f92b4e543390704139ae639193cd1079bdfd639f2340","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-05-22T16:32:27Z","title_canon_sha256":"bbaf40650b54fbc0abc360f185fbf2167cf2b5b03fde1eb29121c8575f457256"},"schema_version":"1.0","source":{"id":"2405.13806","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.13806","created_at":"2026-07-05T11:03:00Z"},{"alias_kind":"arxiv_version","alias_value":"2405.13806v2","created_at":"2026-07-05T11:03:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.13806","created_at":"2026-07-05T11:03:00Z"},{"alias_kind":"pith_short_12","alias_value":"RRHW756RZJNH","created_at":"2026-07-05T11:03:00Z"},{"alias_kind":"pith_short_16","alias_value":"RRHW756RZJNHX5NS","created_at":"2026-07-05T11:03:00Z"},{"alias_kind":"pith_short_8","alias_value":"RRHW756R","created_at":"2026-07-05T11:03:00Z"}],"graph_snapshots":[{"event_id":"sha256:57963a7375b9549b10a6ad6c5a343ea96e4f0d9fc314a1591e79f6eb102a68c5","target":"graph","created_at":"2026-07-05T11:03:00Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2405.13806/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"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","authors_text":"Francesco Di Giovanni, Michael M. Bronstein, Nian Liu, Thomas Laurent, Xavier Bresson, Xiaoxin He","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-05-22T16:32:27Z","title":"A General Graph Spectral Wavelet Convolution via Chebyshev Order Decomposition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.13806","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:f0bc71f7b890dab8901783ae2f7913f457d9fa0f5395f03117376cbd45a6c0ef","target":"record","created_at":"2026-07-05T11:03:00Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"ca46af6f0146cfbc1032f92b4e543390704139ae639193cd1079bdfd639f2340","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-05-22T16:32:27Z","title_canon_sha256":"bbaf40650b54fbc0abc360f185fbf2167cf2b5b03fde1eb29121c8575f457256"},"schema_version":"1.0","source":{"id":"2405.13806","kind":"arxiv","version":2}},"canonical_sha256":"8c4f6ff7d1ca5a7bf5b27aae552c80b4cfa645adde9fa6d2e3f12abeca30baff","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8c4f6ff7d1ca5a7bf5b27aae552c80b4cfa645adde9fa6d2e3f12abeca30baff","first_computed_at":"2026-07-05T11:03:00.494291Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:03:00.494291Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"W2lPL91xmx+whk2oJTWFzNXQpzDqy4EKQweaJhnrW3Y9cMXoJxoQ0xYtIxcFelhuCkqinNow2KpYZEnWxqfnCg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:03:00.494821Z","signed_message":"canonical_sha256_bytes"},"source_id":"2405.13806","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f0bc71f7b890dab8901783ae2f7913f457d9fa0f5395f03117376cbd45a6c0ef","sha256:57963a7375b9549b10a6ad6c5a343ea96e4f0d9fc314a1591e79f6eb102a68c5"],"state_sha256":"f1c1602185e6a084113dde63274e0f9883ea340c8233a9c390d7db7f21aa24c6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"g1hOpFLtcGZ/AbBj1CFjo1pMAm8Xrhc1Pr/yHX8LpkFM1ApWaLcOjgvS1fwsAFW0cmT1fLq46sKWSlabz4e3Dg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T06:10:05.749119Z","bundle_sha256":"c20e835a8a64522ee4a9f241f08b50f451c3a23867c0bfde4e62cdab43277392"}}