{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:MWOODVE4PIVQW722HYPB6ISY3M","short_pith_number":"pith:MWOODVE4","schema_version":"1.0","canonical_sha256":"659ce1d49c7a2b0b7f5a3e1e1f2258db172c13d0c8b4466e16130253bad13887","source":{"kind":"arxiv","id":"2406.08993","version":2},"attestation_state":"computed","paper":{"title":"Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Lei Shi, Xiao-Ming Wu, Yuankai Luo","submitted_at":"2024-06-13T10:53:33Z","abstract_excerpt":"Graph Transformers (GTs) have recently emerged as popular alternatives to traditional message-passing Graph Neural Networks (GNNs), due to their theoretically superior expressiveness and impressive performance reported on standard node classification benchmarks, often significantly outperforming GNNs. In this paper, we conduct a thorough empirical analysis to reevaluate the performance of three classic GNN models (GCN, GAT, and GraphSAGE) against GTs. Our findings suggest that the previously reported superiority of GTs may have been overstated due to suboptimal hyperparameter configurations in"},"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":"2406.08993","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-06-13T10:53:33Z","cross_cats_sorted":[],"title_canon_sha256":"42987804a2e922f7cde58c1fffd1dc80416a35e2f150db081758b1db674f8e7d","abstract_canon_sha256":"297dc8c356b5d30c8a9bcf397c659384d92e9ded1d57ba0b16b67a936d6ed16c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:27:02.141123Z","signature_b64":"rIUMRCyXBps8T/H2yRHUiKAMv0DHygasHR6vTsZU/ThuXEHL0C2N8oNneO0LTW7HZK33Uy9aCg/OdndME8xsCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"659ce1d49c7a2b0b7f5a3e1e1f2258db172c13d0c8b4466e16130253bad13887","last_reissued_at":"2026-07-05T09:27:02.140642Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:27:02.140642Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Lei Shi, Xiao-Ming Wu, Yuankai Luo","submitted_at":"2024-06-13T10:53:33Z","abstract_excerpt":"Graph Transformers (GTs) have recently emerged as popular alternatives to traditional message-passing Graph Neural Networks (GNNs), due to their theoretically superior expressiveness and impressive performance reported on standard node classification benchmarks, often significantly outperforming GNNs. In this paper, we conduct a thorough empirical analysis to reevaluate the performance of three classic GNN models (GCN, GAT, and GraphSAGE) against GTs. Our findings suggest that the previously reported superiority of GTs may have been overstated due to suboptimal hyperparameter configurations in"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.08993","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/2406.08993/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":"2406.08993","created_at":"2026-07-05T09:27:02.140701+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.08993v2","created_at":"2026-07-05T09:27:02.140701+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.08993","created_at":"2026-07-05T09:27:02.140701+00:00"},{"alias_kind":"pith_short_12","alias_value":"MWOODVE4PIVQ","created_at":"2026-07-05T09:27:02.140701+00:00"},{"alias_kind":"pith_short_16","alias_value":"MWOODVE4PIVQW722","created_at":"2026-07-05T09:27:02.140701+00:00"},{"alias_kind":"pith_short_8","alias_value":"MWOODVE4","created_at":"2026-07-05T09:27:02.140701+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.24958","citing_title":"Swarm-Inspired Generation of Collective Behaviors in Graph Dynamical Systems","ref_index":21,"is_internal_anchor":false},{"citing_arxiv_id":"2506.01404","citing_title":"Quantitative Error Feedback for Quantization Noise Reduction of Filtering over Graphs","ref_index":13,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MWOODVE4PIVQW722HYPB6ISY3M","json":"https://pith.science/pith/MWOODVE4PIVQW722HYPB6ISY3M.json","graph_json":"https://pith.science/api/pith-number/MWOODVE4PIVQW722HYPB6ISY3M/graph.json","events_json":"https://pith.science/api/pith-number/MWOODVE4PIVQW722HYPB6ISY3M/events.json","paper":"https://pith.science/paper/MWOODVE4"},"agent_actions":{"view_html":"https://pith.science/pith/MWOODVE4PIVQW722HYPB6ISY3M","download_json":"https://pith.science/pith/MWOODVE4PIVQW722HYPB6ISY3M.json","view_paper":"https://pith.science/paper/MWOODVE4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.08993&json=true","fetch_graph":"https://pith.science/api/pith-number/MWOODVE4PIVQW722HYPB6ISY3M/graph.json","fetch_events":"https://pith.science/api/pith-number/MWOODVE4PIVQW722HYPB6ISY3M/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MWOODVE4PIVQW722HYPB6ISY3M/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MWOODVE4PIVQW722HYPB6ISY3M/action/storage_attestation","attest_author":"https://pith.science/pith/MWOODVE4PIVQW722HYPB6ISY3M/action/author_attestation","sign_citation":"https://pith.science/pith/MWOODVE4PIVQW722HYPB6ISY3M/action/citation_signature","submit_replication":"https://pith.science/pith/MWOODVE4PIVQW722HYPB6ISY3M/action/replication_record"}},"created_at":"2026-07-05T09:27:02.140701+00:00","updated_at":"2026-07-05T09:27:02.140701+00:00"}