Network Representation Learning Based on Community and Text Features

Abstract

Network representation learning (NRL) aims at building a low-dimensional vector for each vertex in a network, which is also increasingly recognized as an important aspect for network analysis. Some current NRL methods only focus on learning representations using the network structure. However, vertices in lots of networks may contain community information or text contents, which could be good for relevant evaluation tasks, such as vertex classification, link prediction and so on. Since it has been proved that DeepWalk is actually equivalent to matrix factorization, we propose community and text-enhanced DeepWalk (CTDW) based on the inductive matrix completion algorithm, which incorporates community features and text features of vertices into NRL under the framework of matrix factorization. In experiments, we evaluate the proposed CTDW compared with other state-of-the-art methods on vertex classification. The experimental results demonstrate that CTDW outperforms other baseline methods on three real-world datasets.