{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:WJYTGNLNEHEHPHGFUCVTOKPN6L","short_pith_number":"pith:WJYTGNLN","canonical_record":{"source":{"id":"2210.16979","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-10-30T23:10:23Z","cross_cats_sorted":[],"title_canon_sha256":"08655b809a6f3c0ccd215be4ad3a6e20c84440ab2f3235157596b9b68baeb700","abstract_canon_sha256":"8ea74acd5ffc38a198335ca7afca1459272afd8192743fe45a1f68f27988b0ca"},"schema_version":"1.0"},"canonical_sha256":"b27133356d21c8779cc5a0ab3729edf2e31cdbf783a9a049bc238ce1fe6c3bbf","source":{"kind":"arxiv","id":"2210.16979","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.16979","created_at":"2026-07-05T07:08:53Z"},{"alias_kind":"arxiv_version","alias_value":"2210.16979v2","created_at":"2026-07-05T07:08:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.16979","created_at":"2026-07-05T07:08:53Z"},{"alias_kind":"pith_short_12","alias_value":"WJYTGNLNEHEH","created_at":"2026-07-05T07:08:53Z"},{"alias_kind":"pith_short_16","alias_value":"WJYTGNLNEHEHPHGF","created_at":"2026-07-05T07:08:53Z"},{"alias_kind":"pith_short_8","alias_value":"WJYTGNLN","created_at":"2026-07-05T07:08:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:WJYTGNLNEHEHPHGFUCVTOKPN6L","target":"record","payload":{"canonical_record":{"source":{"id":"2210.16979","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-10-30T23:10:23Z","cross_cats_sorted":[],"title_canon_sha256":"08655b809a6f3c0ccd215be4ad3a6e20c84440ab2f3235157596b9b68baeb700","abstract_canon_sha256":"8ea74acd5ffc38a198335ca7afca1459272afd8192743fe45a1f68f27988b0ca"},"schema_version":"1.0"},"canonical_sha256":"b27133356d21c8779cc5a0ab3729edf2e31cdbf783a9a049bc238ce1fe6c3bbf","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:08:53.402388Z","signature_b64":"AJbTA42KAVcc8ePK3PEaqmObXPbAKfKfIg5dSNLRdh5oH9h/SEK+V4m8pJCF9Abid4yMw9eCS2+qhyRXdP05DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b27133356d21c8779cc5a0ab3729edf2e31cdbf783a9a049bc238ce1fe6c3bbf","last_reissued_at":"2026-07-05T07:08:53.401841Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:08:53.401841Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2210.16979","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":"2026-07-05T07:08:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fZvppDzc5rW9a9tJmEfDIAgn2gJr2EtpbXCI2ZmwQ+Ql2cCJ6DGq/QQZpBkl6C1w30GjJ29U1PyvqJ9yS2iZCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T22:23:27.799163Z"},"content_sha256":"303e0159099afbf16e53dbf19aaca279e77156c3a2112867194b53755096cfff","schema_version":"1.0","event_id":"sha256:303e0159099afbf16e53dbf19aaca279e77156c3a2112867194b53755096cfff"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:WJYTGNLNEHEHPHGFUCVTOKPN6L","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"When Do We Need Graph Neural Networks for Node Classification?","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Chenqing Hua, Doina Precup, Jiaqi Zhu, Qincheng Lu, Sitao Luan, Xiao-Wen Chang","submitted_at":"2022-10-30T23:10:23Z","abstract_excerpt":"Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by additionally making use of graph structure based on the relational inductive bias (edge bias), rather than treating the nodes as collections of independent and identically distributed (i.i.d.) samples. Though GNNs are believed to outperform basic NNs in real-world tasks, it is found that in some cases, GNNs have little performance gain or even underperform graph-agnostic NNs. To identify these cases, based on graph signal processing and statistical hypothesis testing, we propose two measures which analyze the cases in which the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.16979","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/2210.16979/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":"2026-07-05T07:08:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fUndfdX8FFsSnkREQKReFe8mK+l+R07JuDebEXAVpEf0rxgnBasLcExXbgr4tUnP+Y8V7sVZ1PVkvRDtGHIJDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T22:23:27.799653Z"},"content_sha256":"a7dccb6953a421f75480fdedccfbe19425e114c9478a345d027a57d68ec5e126","schema_version":"1.0","event_id":"sha256:a7dccb6953a421f75480fdedccfbe19425e114c9478a345d027a57d68ec5e126"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WJYTGNLNEHEHPHGFUCVTOKPN6L/bundle.json","state_url":"https://pith.science/pith/WJYTGNLNEHEHPHGFUCVTOKPN6L/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WJYTGNLNEHEHPHGFUCVTOKPN6L/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":"2026-08-16T22:23:27Z","links":{"resolver":"https://pith.science/pith/WJYTGNLNEHEHPHGFUCVTOKPN6L","bundle":"https://pith.science/pith/WJYTGNLNEHEHPHGFUCVTOKPN6L/bundle.json","state":"https://pith.science/pith/WJYTGNLNEHEHPHGFUCVTOKPN6L/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WJYTGNLNEHEHPHGFUCVTOKPN6L/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:WJYTGNLNEHEHPHGFUCVTOKPN6L","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":"8ea74acd5ffc38a198335ca7afca1459272afd8192743fe45a1f68f27988b0ca","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-10-30T23:10:23Z","title_canon_sha256":"08655b809a6f3c0ccd215be4ad3a6e20c84440ab2f3235157596b9b68baeb700"},"schema_version":"1.0","source":{"id":"2210.16979","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.16979","created_at":"2026-07-05T07:08:53Z"},{"alias_kind":"arxiv_version","alias_value":"2210.16979v2","created_at":"2026-07-05T07:08:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.16979","created_at":"2026-07-05T07:08:53Z"},{"alias_kind":"pith_short_12","alias_value":"WJYTGNLNEHEH","created_at":"2026-07-05T07:08:53Z"},{"alias_kind":"pith_short_16","alias_value":"WJYTGNLNEHEHPHGF","created_at":"2026-07-05T07:08:53Z"},{"alias_kind":"pith_short_8","alias_value":"WJYTGNLN","created_at":"2026-07-05T07:08:53Z"}],"graph_snapshots":[{"event_id":"sha256:a7dccb6953a421f75480fdedccfbe19425e114c9478a345d027a57d68ec5e126","target":"graph","created_at":"2026-07-05T07:08:53Z","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/2210.16979/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by additionally making use of graph structure based on the relational inductive bias (edge bias), rather than treating the nodes as collections of independent and identically distributed (i.i.d.) samples. Though GNNs are believed to outperform basic NNs in real-world tasks, it is found that in some cases, GNNs have little performance gain or even underperform graph-agnostic NNs. To identify these cases, based on graph signal processing and statistical hypothesis testing, we propose two measures which analyze the cases in which the","authors_text":"Chenqing Hua, Doina Precup, Jiaqi Zhu, Qincheng Lu, Sitao Luan, Xiao-Wen Chang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-10-30T23:10:23Z","title":"When Do We Need Graph Neural Networks for Node Classification?"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.16979","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":"sha256:303e0159099afbf16e53dbf19aaca279e77156c3a2112867194b53755096cfff","target":"record","created_at":"2026-07-05T07:08:53Z","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":"8ea74acd5ffc38a198335ca7afca1459272afd8192743fe45a1f68f27988b0ca","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-10-30T23:10:23Z","title_canon_sha256":"08655b809a6f3c0ccd215be4ad3a6e20c84440ab2f3235157596b9b68baeb700"},"schema_version":"1.0","source":{"id":"2210.16979","kind":"arxiv","version":2}},"canonical_sha256":"b27133356d21c8779cc5a0ab3729edf2e31cdbf783a9a049bc238ce1fe6c3bbf","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b27133356d21c8779cc5a0ab3729edf2e31cdbf783a9a049bc238ce1fe6c3bbf","first_computed_at":"2026-07-05T07:08:53.401841Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:08:53.401841Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"AJbTA42KAVcc8ePK3PEaqmObXPbAKfKfIg5dSNLRdh5oH9h/SEK+V4m8pJCF9Abid4yMw9eCS2+qhyRXdP05DA==","signature_status":"signed_v1","signed_at":"2026-07-05T07:08:53.402388Z","signed_message":"canonical_sha256_bytes"},"source_id":"2210.16979","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:303e0159099afbf16e53dbf19aaca279e77156c3a2112867194b53755096cfff","sha256:a7dccb6953a421f75480fdedccfbe19425e114c9478a345d027a57d68ec5e126"],"state_sha256":"92921d36cd1e47dcca311593c4ca07c88b1e6e43b1a50a4951c98fedc05ec833"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"//EFZazME19BJm4wLuyfpz5TNROc8nngWYmTqtL68hOB42ZznfplaGGJIsZ4ialCnoS0yHCnwurDPzvv429ZDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-16T22:23:27.804248Z","bundle_sha256":"e5c174a70d4de7327163720beffd868cc79a64849fac75d52d0730233bf3c2bb"}}