解释图神经网络以理解节点分类中的加权图特征
Explain Graph Neural Networks to Understand Weighted Graph Features in Node Classification
摘要：
从不同的应用程序，具有额外的拓扑结构和连接信息收集的真实数据是服从表示为加权图。图神经网络（GNNs）是研究节点标注问题的一种强有力的工具，它可以模拟专家对节点标注的决策。GNN通过使用神经网络来嵌入节点信息并通过图中的边将其传递，从而将节点特征、连接模式和图结构相结合。我们希望识别GNN模型使用的输入数据中的模式，以做出决策，并检查模型是否按我们的期望工作。然而，由于复杂的数据表示和非线性变换，解释GNN做出的决策是具有挑战性的。在这项工作中，我们提出了新的图形特征的解释方法，以确定信息组件和重要的节点特征。此外，我们提出了一个管道，以确定用于节点分类的关键因素。我们使用四个数据集（两个合成和两个真实）来验证我们的方法。我们的研究结果表明，我们的解释方法可以模仿用于节点分类的数据模式，通过人类的解释和解开图中的不同功能。此外，我们的解释方法可用于理解数据，调试GNN模型，并检查模型决策。
英文摘要：
Real data collected from different applications that have additional topological structures and connection information are amenable to be represented as a weighted graph. Considering the node labeling problem, Graph Neural Networks (GNNs) is a powerful tool, which can mimic experts' decision on node labeling. GNNs combine node features, connection patterns, and graph structure by using a neural network to embed node information and pass it through edges in the graph. We want to identify the patterns in the input data used by the GNN model to make a decision and examine if the model works as we desire. However, due to the complex data representation and non-linear transformations, explaining decisions made by GNNs is challenging. In this work, we propose new graph features' explanation methods to identify the informative components and important node features. Besides, we propose a pipeline to identify the key factors used for node classification. We use four datasets (two synthetic and two real) to validate our methods. Our results demonstrate that our explanation approach can mimic data patterns used for node classification by human interpretation and disentangle different features in the graphs. Furthermore, our explanation methods can be used for understanding data, debugging GNN models, and examine model decisions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
