Relational Graph Convolutional Networks: A Closer Look

Tracking #: 2846-4060

This paper is currently under review
Authors: 
Thiviyan Thanapalasingam
Lucas van Berkel
Peter Bloem
Paul Groth

Responsible editor: 
Claudia d'Amato

Submission type: 
Full Paper
Abstract: 
In this paper, 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 https://github.com/thiviyanT/torch-rgcn.
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Under Review