Boosting Hidden Graph Node Classification for Large Social Networks

Hanxuan Yang, Qingchao Kong, Wenji Mao, Lei Wang

Abstract: Identifying hidden nodes in social networks is a critical issue in security-related applications. In contrast to the conventional node classification on graphs with all nodes being observable, it is more challenging to classify the hidden nodes that are unobservable during the training process, also known as the “inductive learning” in previous research. Existing approaches for inductive node classification mainly adopt graph neural network models to learn node representations. Although these methods are advantageous to modeling the topology of graph-structured data, they rely heavily on node features which may vary significantly in different specific application scenarios. In addition, the inherently changeable graph structure induced by hidden nodes may cause the over-fitting problem. To address the above issues and boost the performances of hidden node classification, we propose a deep generative model based on variational auto-encoders. Specifically, we design a novel graph neural network to aggregate the multi-hop neighbor information of each node. Meanwhile, to better utilize the graph structure information as a supplement to node features, we consider the heterogeneous node influences and introduce a gated attention mechanism using node degrees. Moreover, our proposed model can be trained by minibatches and thus is applicable to large social networks. We conduct experiments on four real-world datasets, and verify the effectiveness of our method for hidden graph node classification.