User-friendly, Interactive, and Configurable Explanations for Graph Neural Networks with Graph Views
Tingyang Chen, Dazhuo Qiu, Yinghui Wu, Arijit Khan, Xiangyu Ke, Yunjun Gao · 2024
Explaining the behavior of graph neural networks (GNNs) has become critical due to their "black-box'' nature, especially in the context of analytical tasks such as graph classification. Current approaches are limited to providing explanations for individual instances or specific class labels and may return large explanation structures that are hard to access, nor directly queryable. In this paper, we present GVEX [1] (Graph Views for GNN EXplanation) -- our system developed to offer user-friendly, interactive, and configurable explanations for GNNs based on graph views.