Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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A Gradient-Based Explanation Method for Node Classification Using Graph Convolutional Networks

  • Chaehyeon Kim ( Dept. of Computer Science, Sookmyung Women's University) ;
  • Hyewon Ryu ( Dept. of Software Convergence, Sookmyung Women's University) ;
  • Ki Yong Lee ( Dept. of Computer Science, Sookmyung Women's University)
  • Received : 2022.08.16
  • Accepted : 2023.01.21
  • Published : 2023.12.31
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Abstract

Explainable artificial intelligence is a method that explains how a complex model (e.g., a deep neural network) yields its output from a given input. Recently, graph-type data have been widely used in various fields, and diverse graph neural networks (GNNs) have been developed for graph-type data. However, methods to explain the behavior of GNNs have not been studied much, and only a limited understanding of GNNs is currently available. Therefore, in this paper, we propose an explanation method for node classification using graph convolutional networks (GCNs), which is a representative type of GNN. The proposed method finds out which features of each node have the greatest influence on the classification of that node using GCN. The proposed method identifies influential features by backtracking the layers of the GCN from the output layer to the input layer using the gradients. The experimental results on both synthetic and real datasets demonstrate that the proposed explanation method accurately identifies the features of each node that have the greatest influence on its classification.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2021R1A2C1012543). This paper is the extended version of the Annual Spring Conference of KIPS (ASK 2022) held in Seoul, Republic of Korea dated May 19-21, 2022 [11].

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