arXiv:2107.10015
공식 코드 · Papers with Code
Relational Graph Convolutional Networks: A Closer Look
원문 초록
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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Demystifying Distributed Training of Graph Neural Networks for Link Prediction
ABG-NAS: Adaptive Bayesian Genetic Neural Architecture Search for Graph Representation Learning
MSGCN: Multiplex Spatial Graph Convolution Network for Interlayer Link Weight Prediction
Hierarchical Vector Quantized Graph Autoencoder with Annealing-Based Code Selection
Balancing Graph Embedding Smoothness in Self-Supervised Learning via Information-Theoretic Decomposition
Integrating Structural and Semantic Signals in Text-Attributed Graphs with BiGTex
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