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- W3187018213 abstract "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 https://github.com/thiviyanT/torch-rgcn ." @default.
- W3187018213 created "2021-08-02" @default.
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- W3187018213 date "2022-11-02" @default.
- W3187018213 modified "2023-10-14" @default.
- W3187018213 title "Relational graph convolutional networks: a closer look" @default.
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- W3187018213 doi "https://doi.org/10.7717/peerj-cs.1073" @default.
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