Network embedding is a classical task which aims to map the nodes of a network to low-dimensional vectors. Most of the previous network embedding methods are trained in an unsupervised scheme. Then the learned node embeddings can be used as inputs of many machine learning tasks such as node classification, attribute inference. However, the discrimination validity of the node embeddings maybe improved by considering the node label information and the node attribute information. Inspired by traditional semi-supervised learning techniques, we explore to train the node embeddings and the node classifiers simultaneously with the text attributes information in a flexible framework. We present NLSTNE (Non-Linear Smoothed Transductive Network Embedding), a transductive network embedding method, whose embeddings are enhanced by modeling the non-linear pairwise similarity between the nodes and the non-linear relationship between the nodes and the text attributes. We use the node classification task to evaluate the quality of node embeddings learned by different models on four real-world network datasets . The experimental results demonstrate that our model outperforms several state-of-the-art network embedding methods.
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