{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T19:10:47Z","timestamp":1775070647005,"version":"3.50.1"},"reference-count":30,"publisher":"World Scientific Pub Co Pte Ltd","issue":"06","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Soft. Eng. Knowl. Eng."],"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:p> The inception of the Relational Graph Convolutional Network (R-GCN) marked a milestone in the Semantic Web domain as a widely cited method that generalizes end-to-end hierarchical representation learning to Knowledge Graphs (KGs). R-GCNs generate representations for nodes of interest by repeatedly aggregating parametrized, relation-specific transformations of their neighbors. However, in this work, it is posited that the R-GCN\u2019s main contribution lies in this \u201cmessage passing\u201d paradigm, rather than the learned weights. To prove this, the \u201cRandom Relational Graph Convolutional Network\u201d (RR-GCN) is introduced, which leaves all parameters untrained and thus constructs node embeddings by aggregating randomly transformed random representations from neighbors. Additionally, the advantage offered by learnable parameters for RR-GCN without completely losing the advantages of random transformations is explored. It is empirically shown that RR-GCNs can compete with fully trained R-GCNs in node classification. <\/jats:p>","DOI":"10.1142\/s0218194025500184","type":"journal-article","created":{"date-parts":[[2025,3,22]],"date-time":"2025-03-22T04:33:25Z","timestamp":1742618005000},"page":"809-834","source":"Crossref","is-referenced-by-count":1,"title":["RR-GCN: Exploring Untrained Random Embeddings for Relational Graphs"],"prefix":"10.1142","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5505-4362","authenticated-orcid":false,"given":"Sandeep","family":"Ramachandra","sequence":"first","affiliation":[{"name":"Internet Technology and Data Science Lab (IDLab), Ghent University - imec, 9000 Ghent, Flanders, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6127-7036","authenticated-orcid":false,"given":"Vic","family":"Degraeve","sequence":"additional","affiliation":[{"name":"Internet Technology and Data Science Lab (IDLab), Ghent University - imec, 9000 Ghent, Flanders, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9531-0623","authenticated-orcid":false,"given":"Gilles","family":"Vandewiele","sequence":"additional","affiliation":[{"name":"Internet Technology and Data Science Lab (IDLab), Ghent University - imec, 9000 Ghent, Flanders, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3488-2334","authenticated-orcid":false,"given":"Bram","family":"Steenwinckel","sequence":"additional","affiliation":[{"name":"Internet Technology and Data Science Lab (IDLab), Ghent University - imec, 9000 Ghent, Flanders, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7865-6793","authenticated-orcid":false,"given":"Sofie Van","family":"Hoecke","sequence":"additional","affiliation":[{"name":"Internet Technology and Data Science Lab (IDLab), Ghent University - imec, 9000 Ghent, Flanders, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2529-5477","authenticated-orcid":false,"given":"Femke","family":"Ongenae","sequence":"additional","affiliation":[{"name":"Internet Technology and Data Science Lab (IDLab), Ghent University - imec, 9000 Ghent, Flanders, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2025,5,24]]},"reference":[{"key":"S0218194025500184BIB001","doi-asserted-by":"publisher","DOI":"10.1016\/j.websem.2016.01.001"},{"key":"S0218194025500184BIB002","first-page":"49","volume-title":"Knowledge Graphs for Explainable Artificial Intelligence: Foundations, Applications and Challenges","author":"Palmonari M.","year":"2020"},{"key":"S0218194025500184BIB004","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.03.015"},{"key":"S0218194025500184BIB005","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-020-00701-z"},{"key":"S0218194025500184BIB006","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467231"},{"key":"S0218194025500184BIB007","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-021-00806-z"},{"issue":"4","key":"S0218194025500184BIB008","first-page":"1","volume":"20","author":"Vandewiele G.","year":"2020","journal-title":"BMC Med. 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