An Attributed Network Representation Learning Method Based on Biased Random Walk

Abstract

Network representation learning(NRL) aims to learn the low-dimensional and continuous vector representations for all nodes in networks, which is used as the input feature for many complex networks analysis tasks. Random walk has a wide range of applications in structure-based network representation learning. In reality, the network contains a lot of attribute information, which brings opportunities and challenges to random walk. Combining attribute information can reconstruct the network better. Based on the above, we propose a robust NRL method called Attributed Network Representation Learning Based on Biased Random Walk (ANRLBRW). This method considers the structure and homogeneity of the network through a biased random walk. It uses the attribute random walk to take care of the node’s attribute information, and combines two random walk sequences to learn the network representation of the node using the skip-gram model. The experimental results on three real attributed networks show that the accuracy of node classification outperforms the state-of-the-art methods on the basis of our proposed network representation learning method.