Learning ladder neural networks for semi-supervised node classification in social network

Abstract

Graph convolutional networks (GCNs) and network embedding are the two main categories of popular methods for Semi-Supervised Node Classification (SSNC) in social network. However, the former is commonly oriented to attributed networks with efficient auxiliary information in nodes. The latter is usually not geared towards specific graph mining tasks. Therefore, these methods often perform poorly for specific tasks in non-attributed networks. To solve the above problems, in this paper, we propose a novel semi-supervised Node Classification method with Ladder Neural Networks named NCLNN for non-attributed network. We first preprocess the graph for capturing the structural information. Then we present and learn a deep ladder neural network for SSNC. Our trained ladder neural networks could combine supervised learning with unsupervised learning in deep neural networks via simultaneously minimizing the sum of supervised and unsupervised loss functions. Extensive experiments on three real-world network datasets demonstrate that the proposed NCLNN substantially outperforms the state-of-the-art methods on SSNC task.