Enhancing attributed network embedding via similarity measure

Network embedding aims to represent network structural and attributed information with low-dimensional vectors, which has been demonstrated to be beneficial for many network analysis tasks, such as link prediction, node classification and visualization. However, nodes in networks are commonly associated with rich contents, which are facilitated to characterize the properties of nodes. Most existing attributed network embedding algorithms tend to learn attribute representations separated from structure representations, which require a subsequent processing of combination. Besides, these traditional approaches ignore the potential high-order proximity introduced by attributes. Motivated by this, we investigate how structures and attributes can be captured simultaneously and introduce similarity measure to preserve high-order proximity in an attributed network. In this paper, we propose a novel attributed network embedding framework, Similarity Enhancing Attributed Network Embedding (SEANE), which jointly preserves structural and attributed information, and adopts similarity measure to enhance the node embedding. We evaluate our proposed framework by using four real-world datasets on link prediction, node classification and nearest nodes searching. The experimental results demonstrate the outperformance of SEANE on link prediction and node classification tasks.