{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:UKBHKCLJEPFMZHA7BG3JJGYRCQ","short_pith_number":"pith:UKBHKCLJ","canonical_record":{"source":{"id":"1907.12972","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-30T14:16:45Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"66a080409202a083edd3052e768aad8fb920bfbfc06e1c8ed62eb207ed68d58c","abstract_canon_sha256":"b1b8fb24d06f05b90ca2d867ee417c5c54472bb284cb9209e312a12e83557efc"},"schema_version":"1.0"},"canonical_sha256":"a28275096923cacc9c1f09b6949b11141c3ede702fc7477498f3b76155c4c2a7","source":{"kind":"arxiv","id":"1907.12972","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.12972","created_at":"2026-07-05T02:48:32Z"},{"alias_kind":"arxiv_version","alias_value":"1907.12972v3","created_at":"2026-07-05T02:48:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.12972","created_at":"2026-07-05T02:48:32Z"},{"alias_kind":"pith_short_12","alias_value":"UKBHKCLJEPFM","created_at":"2026-07-05T02:48:32Z"},{"alias_kind":"pith_short_16","alias_value":"UKBHKCLJEPFMZHA7","created_at":"2026-07-05T02:48:32Z"},{"alias_kind":"pith_short_8","alias_value":"UKBHKCLJ","created_at":"2026-07-05T02:48:32Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:UKBHKCLJEPFMZHA7BG3JJGYRCQ","target":"record","payload":{"canonical_record":{"source":{"id":"1907.12972","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-30T14:16:45Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"66a080409202a083edd3052e768aad8fb920bfbfc06e1c8ed62eb207ed68d58c","abstract_canon_sha256":"b1b8fb24d06f05b90ca2d867ee417c5c54472bb284cb9209e312a12e83557efc"},"schema_version":"1.0"},"canonical_sha256":"a28275096923cacc9c1f09b6949b11141c3ede702fc7477498f3b76155c4c2a7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:48:32.162221Z","signature_b64":"kAcRn01FlyFQOvVSlHllOD+THIs6ypOhqLzVOWT0WjGTlSObKcW58LpnYBpBrfmsSqKNPur66Radd8CQsJcmCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a28275096923cacc9c1f09b6949b11141c3ede702fc7477498f3b76155c4c2a7","last_reissued_at":"2026-07-05T02:48:32.161764Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:48:32.161764Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1907.12972","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:48:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rBeUVxD2uUC55Y7rlfh1u0YvQKwrKg4PjBs6kdhyL/YkQH2TsSAjtKaJASsRBDRS7sVvNbbTGI3Rdalgc9TXAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T09:10:00.925643Z"},"content_sha256":"ccbffc09c03f730ba230d48b7d12a2537aa4fd957d7bcdf1060646dc17347d4f","schema_version":"1.0","event_id":"sha256:ccbffc09c03f730ba230d48b7d12a2537aa4fd957d7bcdf1060646dc17347d4f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:UKBHKCLJEPFMZHA7BG3JJGYRCQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Transferability of Spectral Graph Convolutional Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Gitta Kutyniok, Lorenzo Bucci, Michael M. Bronstein, Ron Levie, Wei Huang","submitted_at":"2019-07-30T14:16:45Z","abstract_excerpt":"This paper focuses on spectral graph convolutional neural networks (ConvNets), where filters are defined as elementwise multiplication in the frequency domain of a graph. In machine learning settings where the dataset consists of signals defined on many different graphs, the trained ConvNet should generalize to signals on graphs unseen in the training set. It is thus important to transfer ConvNets between graphs. Transferability, which is a certain type of generalization capability, can be loosely defined as follows: if two graphs describe the same phenomenon, then a single filter or ConvNet s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.12972","kind":"arxiv","version":3},"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/1907.12972/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":"2026-07-05T02:48:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YXaGHeLATXDrTVSTzkdxbDnH4wI33rOAnilHe/zRCX8X9OYa2+3AXAXzwb1VJfU0cDzmxKnKL93lqCmdPdAPDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T09:10:00.926153Z"},"content_sha256":"6fff7f312133adb7f1925443ea25375d21b60d7da70783caa80dbc6ed4898502","schema_version":"1.0","event_id":"sha256:6fff7f312133adb7f1925443ea25375d21b60d7da70783caa80dbc6ed4898502"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UKBHKCLJEPFMZHA7BG3JJGYRCQ/bundle.json","state_url":"https://pith.science/pith/UKBHKCLJEPFMZHA7BG3JJGYRCQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UKBHKCLJEPFMZHA7BG3JJGYRCQ/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":"2026-08-10T09:10:00Z","links":{"resolver":"https://pith.science/pith/UKBHKCLJEPFMZHA7BG3JJGYRCQ","bundle":"https://pith.science/pith/UKBHKCLJEPFMZHA7BG3JJGYRCQ/bundle.json","state":"https://pith.science/pith/UKBHKCLJEPFMZHA7BG3JJGYRCQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UKBHKCLJEPFMZHA7BG3JJGYRCQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:UKBHKCLJEPFMZHA7BG3JJGYRCQ","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":"b1b8fb24d06f05b90ca2d867ee417c5c54472bb284cb9209e312a12e83557efc","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-30T14:16:45Z","title_canon_sha256":"66a080409202a083edd3052e768aad8fb920bfbfc06e1c8ed62eb207ed68d58c"},"schema_version":"1.0","source":{"id":"1907.12972","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.12972","created_at":"2026-07-05T02:48:32Z"},{"alias_kind":"arxiv_version","alias_value":"1907.12972v3","created_at":"2026-07-05T02:48:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.12972","created_at":"2026-07-05T02:48:32Z"},{"alias_kind":"pith_short_12","alias_value":"UKBHKCLJEPFM","created_at":"2026-07-05T02:48:32Z"},{"alias_kind":"pith_short_16","alias_value":"UKBHKCLJEPFMZHA7","created_at":"2026-07-05T02:48:32Z"},{"alias_kind":"pith_short_8","alias_value":"UKBHKCLJ","created_at":"2026-07-05T02:48:32Z"}],"graph_snapshots":[{"event_id":"sha256:6fff7f312133adb7f1925443ea25375d21b60d7da70783caa80dbc6ed4898502","target":"graph","created_at":"2026-07-05T02:48:32Z","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/1907.12972/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper focuses on spectral graph convolutional neural networks (ConvNets), where filters are defined as elementwise multiplication in the frequency domain of a graph. In machine learning settings where the dataset consists of signals defined on many different graphs, the trained ConvNet should generalize to signals on graphs unseen in the training set. It is thus important to transfer ConvNets between graphs. Transferability, which is a certain type of generalization capability, can be loosely defined as follows: if two graphs describe the same phenomenon, then a single filter or ConvNet s","authors_text":"Gitta Kutyniok, Lorenzo Bucci, Michael M. Bronstein, Ron Levie, Wei Huang","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-30T14:16:45Z","title":"Transferability of Spectral Graph Convolutional Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.12972","kind":"arxiv","version":3},"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:ccbffc09c03f730ba230d48b7d12a2537aa4fd957d7bcdf1060646dc17347d4f","target":"record","created_at":"2026-07-05T02:48:32Z","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":"b1b8fb24d06f05b90ca2d867ee417c5c54472bb284cb9209e312a12e83557efc","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-30T14:16:45Z","title_canon_sha256":"66a080409202a083edd3052e768aad8fb920bfbfc06e1c8ed62eb207ed68d58c"},"schema_version":"1.0","source":{"id":"1907.12972","kind":"arxiv","version":3}},"canonical_sha256":"a28275096923cacc9c1f09b6949b11141c3ede702fc7477498f3b76155c4c2a7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a28275096923cacc9c1f09b6949b11141c3ede702fc7477498f3b76155c4c2a7","first_computed_at":"2026-07-05T02:48:32.161764Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:48:32.161764Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kAcRn01FlyFQOvVSlHllOD+THIs6ypOhqLzVOWT0WjGTlSObKcW58LpnYBpBrfmsSqKNPur66Radd8CQsJcmCg==","signature_status":"signed_v1","signed_at":"2026-07-05T02:48:32.162221Z","signed_message":"canonical_sha256_bytes"},"source_id":"1907.12972","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ccbffc09c03f730ba230d48b7d12a2537aa4fd957d7bcdf1060646dc17347d4f","sha256:6fff7f312133adb7f1925443ea25375d21b60d7da70783caa80dbc6ed4898502"],"state_sha256":"0bf744efc4683e40ba2779bf5c73297cc48bbceeb209fc835f1782dac17f0f73"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VPGmjEROkm7OqNR+kx4B/vzCDBx5/REtHxN5a+gMTf29q0bo2u5ctU9YYWgCTOLyqk9V/w9hRUlVdG5+aueLAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T09:10:00.929603Z","bundle_sha256":"21b5d4a6afe4bdbf881e5a85566d8088414a0ba2ca0c44f37a2d4d6dd54d4b4f"}}