Relational Graph Convolutional Networks: A Closer Look

• URL: http://arxiv.org/abs/2107.10015v1
• Date: Wed, 21 Jul 2021 11:25:11 GMT
• Title: Relational Graph Convolutional Networks: A Closer Look
• Authors: Thiviyan Thanapalasingam, Lucas van Berkel, Peter Bloem, Paul Groth
• Abstract summary: We describe a reproduction of the Graph Convolutional Network (RGCN) Using our reproduction, we explain the intuition behind the model. Our results empirically validate the correctness of our implementations.
• Score: 1.8428580623654864
• License: http://creativecommons.org/licenses/by/4.0/
• 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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