论文标题
可解释的图形神经网络的调查：分类学和评估指标
A Survey of Explainable Graph Neural Networks: Taxonomy and Evaluation Metrics
论文作者
论文摘要
图形神经网络（GNN）已证明图形数据的预测性能显着提高。同时，这些模型的预测通常很难解释。在这方面，已经做出了许多努力来从gnnexplainer，xgnn和pgexplainer等角度解释这些模型的预测机制。尽管这样的作品呈现出系统的框架来解释GNN，但无法解释的GNN的整体评论是不可用的。在这项调查中，我们介绍了针对GNN开发的解释性技术的全面综述。我们专注于可解释的图形神经网络，并根据可解释方法的使用对它们进行分类。我们进一步为GNNS解释提供了共同的性能指标，并指出了几个未来的研究指标。
Graph neural networks (GNNs) have demonstrated a significant boost in prediction performance on graph data. At the same time, the predictions made by these models are often hard to interpret. In that regard, many efforts have been made to explain the prediction mechanisms of these models from perspectives such as GNNExplainer, XGNN and PGExplainer. Although such works present systematic frameworks to interpret GNNs, a holistic review for explainable GNNs is unavailable. In this survey, we present a comprehensive review of explainability techniques developed for GNNs. We focus on explainable graph neural networks and categorize them based on the use of explainable methods. We further provide the common performance metrics for GNNs explanations and point out several future research directions.
