Graph Neural Networks (GNN) are powerful tools for deep learning. Similar to other neural networks, GNNs are complex models, in which humans can’t understand the decision-making procedures of the models. Therefore, it brings the need to explainability of GNNs. Explainability is critical for deep learning to support its predictions. In this paper, we will investigate the Grad-Cam and Integrated-Gradients explaining methods. The Grad-Cam applies a global average pooling over the feature activation mapping, and then which was followed by a ReLU activation to obtain an attribution. The Integrated-Gradients explains models by taking a line integral between the baseline image (a black image) and the source image. We demonstrate how Grad-Cam and the Integrated-Gradients methods explain the graph-deep model in semantic segmentation tasks over the Cityscapes dataset. FCN and LRASSP-MobileNet are used as a comparison to the DualGCN in the experiment to show the explaining effect.

中文翻译：

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基于 GNN 的分割网络的解释器

图神经网络（GNN）是深度学习的强大工具。与其他神经网络类似，GNN 是复杂的模型，人类无法理解模型的决策过程。因此，这就需要 GNN 的可解释性。可解释性对于深度学习支持其预测至关重要。在本文中，我们将研究 Grad-Cam 和 Integrated-Gradients 解释方法。 Grad-Cam 在特征激活映射上应用全局平均池化，然后进行 ReLU 激活以获得归因。积分梯度通过在基线图像（黑色图像）和源图像之间进行线积分来解释模型。我们演示了 Grad-Cam 和 Integrated-Gradients 方法如何解释 Cityscapes 数据集语义分割任务中的图深度模型。实验中使用FCN和LRASSP-MobileNet与DualGCN进行对比，以展示解释效果。