{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/r-gcn-the-r-could-stand-for-random","title":"R-GCN: The R Could Stand for Random","arxiv_id":"2203.02424","date":"2022-03-04","proceeding":null,"authors":["Vic Degraeve","Gilles Vandewiele","Femke Ongenae","Sofie Van Hoecke"],"abstract":"The inception of the Relational Graph Convolutional Network (R-GCN) marked a milestone in the Semantic Web domain as a widely cited method that generalises end-to-end hierarchical representation learning to Knowledge Graphs (KGs). R-GCNs generate representations for nodes of interest by repeatedly aggregating parameterised, relation-specific transformations of their neighbours. However, in this paper, we argue that the the R-GCN's main contribution lies in this \"message passing\" paradigm, rather than the learned weights. To this end, we introduce the \"Random Relational Graph Convolutional Network\" (RR-GCN), which leaves all parameters untrained and thus constructs node embeddings by aggregating randomly transformed random representations from neighbours, i.e., with no learned parameters. We empirically show that RR-GCNs can compete with fully trained R-GCNs in both node classification and link prediction settings.","url_abs":"https://arxiv.org/abs/2203.02424v2","url_pdf":"https://arxiv.org/pdf/2203.02424v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"r-gcn-the-r-could-stand-for-random","repo_url":"https://github.com/predict-idlab/RR-GCN","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"knowledge-graphs","task_name":"Knowledge Graphs"},{"task_slug":"link-prediction","task_name":"Link Prediction"},{"task_slug":"node-classification","task_name":"Node Classification"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/link-prediction-on-fb15k-237","task":"Link Prediction","dataset":"FB15k-237","model":"RR-GCN-PPV","rank_in_archive_order":54,"of":75,"metrics":{"Hits@1":"0.157","Hits@10":"0.412","Hits@3":"0.256","MRR":"0.238"},"uses_additional_data":false},{"leaderboard":"/sota/node-classification-on-aifb","task":"Node Classification","dataset":"AIFB","model":"RR-GCN-PPV-CUT","rank_in_archive_order":2,"of":7,"metrics":{"Accuracy":"95.83"},"uses_additional_data":false},{"leaderboard":"/sota/node-classification-on-aifb","task":"Node Classification","dataset":"AIFB","model":"RR-GCN-PPV","rank_in_archive_order":7,"of":7,"metrics":{"Accuracy":"86.11"},"uses_additional_data":false},{"leaderboard":"/sota/node-classification-on-am","task":"Node Classification","dataset":"AM","model":"RR-GCN-PPV-CUT (Unimportant relations 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