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.

图卷积网络（GCN）和网络嵌入是社交网络中半监督节点分类（SSNC）的流行方法的两大类。但是，前者通常面向节点中具有有效辅助信息的属性网络。后者通常不适合特定的图挖掘任务。因此，这些方法对于非归属网络中的特定任务通常效果不佳。为了解决上述问题，本文提出了一种新的基于梯形神经网络的半监督节点分类方法NCLNN。我们首先对图形进行预处理以捕获结构信息。然后，我们介绍并学习用于SSNC的深层梯级神经网络。我们训练有素的梯形神经网络可以通过同时最小化有监督和无监督损失函数的总和，将监督学习与深度神经网络中的无监督学习结合起来。在三个实际网络数据集上进行的大量实验表明，所提出的NCLNN明显优于SSNC任务的最新方法。