Deepwalk-assisted Graph PCA (DGPCA) for Language Networks

Abstract

Language graph learning is an important task with many applications such as text classification, link prediction and community detection. One of the challenges in this domain is finding an efficient way to learn and encode graph into a low dimensional embedding. In this paper, a novel DeepWalk-assisted Graph PCA (DGPCA) method is proposed for processing language network data represented by graphs. This method can generate a precise text representation for nodes (or vertices) in language networks. Unlike other existing work, our learned low dimensional vector representations add flexibility in exploring vertices' neighborhood information, while reducing noise contained in the original data. To demonstrate the effectiveness, we use DGPCA to classify vertices that contain text information in three language networks. Experimentally, DGPCA is shown to perform well on the language datasets in comparison to several state-of-the-art benchmarking methods.