Interpreting and Understanding Graph Convolutional Neural Network using Gradient-based Attribution Method

9 Mar 2019Shangsheng XieMingming Lu

To solve the problem that convolutional neural networks (CNNs) are difficult to process non-grid type relational data like graphs, Kipf et al. proposed a graph convolutional neural network (GCN). The core idea of the GCN is to perform two-fold informational fusion for each node in a given graph during each iteration: the fusion of graph structure information and the fusion of node feature dimensions... (read more)

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