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Medientyp:
E-Artikel
Titel:
Relational graph convolutional networks: a closer look
Beteiligte:
Thanapalasingam, Thiviyan;
van Berkel, Lucas;
Bloem, Peter;
Groth, Paul
Erschienen:
PeerJ, 2022
Erschienen in:PeerJ Computer Science
Sprache:
Englisch
DOI:
10.7717/peerj-cs.1073
ISSN:
2376-5992
Entstehung:
Anmerkungen:
Beschreibung:
<jats:p>In this article, we describe a reproduction of the Relational Graph Convolutional Network (RGCN). Using our reproduction, we explain the intuition behind the model. Our reproduction results empirically validate the correctness of our implementations using benchmark Knowledge Graph datasets on node classification and link prediction tasks. Our explanation provides a friendly understanding of the different components of the RGCN for both users and researchers extending the RGCN approach. Furthermore, we introduce two new configurations of the RGCN that are more parameter efficient. The code and datasets are available at <jats:uri xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://github.com/thiviyanT/torch-rgcn">https://github.com/thiviyanT/torch-rgcn</jats:uri>.</jats:p>