{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:KZFIYHS4HT7LSO3IHB3AXAUPSO","short_pith_number":"pith:KZFIYHS4","schema_version":"1.0","canonical_sha256":"564a8c1e5c3cfeb93b6838760b828f93b423fb9d4b17db4f49da205dc7096f51","source":{"kind":"arxiv","id":"2206.00362","version":4},"attestation_state":"computed","paper":{"title":"An Empirical Study of Retrieval-enhanced Graph Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Bernardo Cuenca Grau, Dingmin Wang, Hanchen Wang, Jian Tang, Linfeng Song, Qi Liu, Shengchao Liu, Song Le","submitted_at":"2022-06-01T09:59:09Z","abstract_excerpt":"Graph Neural Networks (GNNs) are effective tools for graph representation learning. Most GNNs rely on a recursive neighborhood aggregation scheme, named message passing, thereby their theoretical expressive power is limited to the first-order Weisfeiler-Lehman test (1-WL). An effective approach to this challenge is to explicitly retrieve some annotated examples used to enhance GNN models. While retrieval-enhanced models have been proved to be effective in many language and vision domains, it remains an open question how effective retrieval-enhanced GNNs are when applied to graph datasets. Moti"},"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":"2206.00362","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-06-01T09:59:09Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"fa2ef556bc2351ba7f6e7a7b77c271d7aa65ac38f0db49434fca72a6c5f6abc0","abstract_canon_sha256":"3e1f132148198ce424e5933ac1267ff195068a63521d2115b8c2b25db5dac119"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:51:18.017786Z","signature_b64":"5/HXc+AmRNQN/M+KjZXFgcmv5udpSSu/Cpm+MnjEdIbZegAEa/Bcowbd8vqXK950HBYntHnlJCKNp0SuysM+Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"564a8c1e5c3cfeb93b6838760b828f93b423fb9d4b17db4f49da205dc7096f51","last_reissued_at":"2026-07-05T06:51:18.017370Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:51:18.017370Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Empirical Study of Retrieval-enhanced Graph Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Bernardo Cuenca Grau, Dingmin Wang, Hanchen Wang, Jian Tang, Linfeng Song, Qi Liu, Shengchao Liu, Song Le","submitted_at":"2022-06-01T09:59:09Z","abstract_excerpt":"Graph Neural Networks (GNNs) are effective tools for graph representation learning. Most GNNs rely on a recursive neighborhood aggregation scheme, named message passing, thereby their theoretical expressive power is limited to the first-order Weisfeiler-Lehman test (1-WL). An effective approach to this challenge is to explicitly retrieve some annotated examples used to enhance GNN models. While retrieval-enhanced models have been proved to be effective in many language and vision domains, it remains an open question how effective retrieval-enhanced GNNs are when applied to graph datasets. Moti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.00362","kind":"arxiv","version":4},"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/2206.00362/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":"2206.00362","created_at":"2026-07-05T06:51:18.017429+00:00"},{"alias_kind":"arxiv_version","alias_value":"2206.00362v4","created_at":"2026-07-05T06:51:18.017429+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.00362","created_at":"2026-07-05T06:51:18.017429+00:00"},{"alias_kind":"pith_short_12","alias_value":"KZFIYHS4HT7L","created_at":"2026-07-05T06:51:18.017429+00:00"},{"alias_kind":"pith_short_16","alias_value":"KZFIYHS4HT7LSO3I","created_at":"2026-07-05T06:51:18.017429+00:00"},{"alias_kind":"pith_short_8","alias_value":"KZFIYHS4","created_at":"2026-07-05T06:51:18.017429+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/KZFIYHS4HT7LSO3IHB3AXAUPSO","json":"https://pith.science/pith/KZFIYHS4HT7LSO3IHB3AXAUPSO.json","graph_json":"https://pith.science/api/pith-number/KZFIYHS4HT7LSO3IHB3AXAUPSO/graph.json","events_json":"https://pith.science/api/pith-number/KZFIYHS4HT7LSO3IHB3AXAUPSO/events.json","paper":"https://pith.science/paper/KZFIYHS4"},"agent_actions":{"view_html":"https://pith.science/pith/KZFIYHS4HT7LSO3IHB3AXAUPSO","download_json":"https://pith.science/pith/KZFIYHS4HT7LSO3IHB3AXAUPSO.json","view_paper":"https://pith.science/paper/KZFIYHS4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2206.00362&json=true","fetch_graph":"https://pith.science/api/pith-number/KZFIYHS4HT7LSO3IHB3AXAUPSO/graph.json","fetch_events":"https://pith.science/api/pith-number/KZFIYHS4HT7LSO3IHB3AXAUPSO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KZFIYHS4HT7LSO3IHB3AXAUPSO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KZFIYHS4HT7LSO3IHB3AXAUPSO/action/storage_attestation","attest_author":"https://pith.science/pith/KZFIYHS4HT7LSO3IHB3AXAUPSO/action/author_attestation","sign_citation":"https://pith.science/pith/KZFIYHS4HT7LSO3IHB3AXAUPSO/action/citation_signature","submit_replication":"https://pith.science/pith/KZFIYHS4HT7LSO3IHB3AXAUPSO/action/replication_record"}},"created_at":"2026-07-05T06:51:18.017429+00:00","updated_at":"2026-07-05T06:51:18.017429+00:00"}