论文标题
探索图形神经网络的解释性方法
Exploring Explainability Methods for Graph Neural Networks
论文作者
论文摘要
随着深度学习方法的日益增长，尤其是图形神经网络（编码复杂的互连信息）对于各种实际任务，因此在这种情况下有必要解释性。在本文中，我们演示了基于图形的超级像素图像分类任务上流行的解释性方法（GAT）上的普遍解释性方法的适用性。我们评估了这些技术在三个不同数据集上的定性和定量性能，并描述了我们的发现。结果阐明了GNN中解释性的概念，尤其是GAT。
With the growing use of deep learning methods, particularly graph neural networks, which encode intricate interconnectedness information, for a variety of real tasks, there is a necessity for explainability in such settings. In this paper, we demonstrate the applicability of popular explainability approaches on Graph Attention Networks (GAT) for a graph-based super-pixel image classification task. We assess the qualitative and quantitative performance of these techniques on three different datasets and describe our findings. The results shed a fresh light on the notion of explainability in GNNs, particularly GATs.
