2023年度 人工知能学会全国大会（第37回）
2023年6月6日〜6月9日熊本城ホール(熊本県熊本市) ＋ オンライン
2023年度 人工知能学会全国大会（第37回）
2023年6月6日〜6月9日熊本城ホール(熊本県熊本市) ＋ オンライン
[2U1-IS-1b-03]Learning Graph Neural Networks with Key Subgraphs using Explanation Confidence
〇Kaito Inoue1, Jianming Huang1, Zhongxi Fang1, Hiroyuki Kasai1,2(1. Department of Computer Science and Communications Engineering, Graduate School of Fundamental Science and Engineering, Waseda University, 2. Department of Communications and Computer Engineering, FSE School, Waseda University)
[[Online, Regular]]
In recent years, Graph Neural Networks (GNNs) have demonstrated significant advances in accuracy for various graph-related tasks. However, GNNs still fail to achieve high performance in graph classification tasks. One of the primary reasons for this is that GNNs cannot learn key subgraphs that contribute to the prediction. Some research on identifying key subgraphs has been conducted within the field of Explainable AI (XAI) in graphs. Especially explanation confidence (EC) is an important evaluation method for XAI models of GNNs. In this paper, we propose a novel method for learning GNNs that incorporates Explanation Confidence (EC). We demonstrate that the proposed method performs as well as or better than conventional methods in graph classification experiments.