Graph Convolutional Network (GCN) has experienced great success in graph analysis tasks. It works by smoothing the node features across the graph. The current GCN models overwhelmingly assume that node feature information is complete. However, real-world graph data are often incomplete and containing missing features. Traditionally, people have to estimate and fill in the unknown features based on imputation techniques and then apply GCN. However, the process of feature filling and graph learning are separated, resulting in degraded and unstable performance. This problem becomes more serious when a large number of features are missing. We propose an approach that adapts GCN to graphs containing missing features. In contrast to traditional strategy, our approach integrates the processing of missing features and graph learning within the same neural network architecture. Our idea is to represent the missing data by Gaussian Mixture Model (GMM) and calculate the expected activation of neurons in the first hidden layer of GCN, while keeping the other layers of the network unchanged. This enables us to learn the GMM parameters and network weight parameters in an end-to-end manner. Notably, our approach does not increase the computational complexity of GCN and it is consistent with GCN when the features are complete. We conduct experiments on the node label classification task and demonstrate that our approach significantly outperforms the best imputation based methods by up to 99.43%, 102.96%, 6.97%, 35.36% in four benchmark graphs when a large portion of features are missing. The performance of our approach for the case with a low level of missing features is even superior to GCN for the case with complete features.

该文提出了一种基于高斯混合模型的图卷积神经网络方法，有效解决了节点特征信息不完整所带来的性能退化问题，实现了端到端的特征填充和图学习过程，并在节点分类和链路预测任务中明显优于基于特征填充技术的方法。

包含缺失特征的图形卷积网络