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

Understand

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.
• Because of the characteristic of the combinatorial generalizations, GCN has been widely used in the fields of scene semantic relationship analysis, natural language processing and few-shot learning etc.