论文标题
图形神经网络中的解释性：实验调查
Explainability in Graph Neural Networks: An Experimental Survey
论文作者
论文摘要
图形神经网络（GNN）已被广泛开发用于在各种应用程序域中学习的图形表示。但是，与所有其他神经网络模型类似，GNNS都遭受了黑盒问题的困扰，因为人们无法理解它们的基础机制。为了解决这个问题，已经提出了几种GNN解释性方法来解释GNNS做出的决定。在这项调查中，我们概述了最新的GNN解释性方法及其评估方式。此外，我们提出了一个新的评估指标，并进行了彻底的实验，以比较现实世界数据集上的GNN解释性方法。我们还建议未来的GNN解释性方向。
Graph neural networks (GNNs) have been extensively developed for graph representation learning in various application domains. However, similar to all other neural networks models, GNNs suffer from the black-box problem as people cannot understand the mechanism underlying them. To solve this problem, several GNN explainability methods have been proposed to explain the decisions made by GNNs. In this survey, we give an overview of the state-of-the-art GNN explainability methods and how they are evaluated. Furthermore, we propose a new evaluation metric and conduct thorough experiments to compare GNN explainability methods on real world datasets. We also suggest future directions for GNN explainability.
