The Network Representation Learning Algorithm Based on Semi-Supervised Random Walk

Abstract

As an important tool of social network analysis, network representation learning also called network embedding maps the network to a latent space and learns low-dimensional and dense real vectors of nodes, while preserving the structure and internal attributes of network. The learned representations or embedding vectors can be used for node clustering, link prediction, network visualization and other tasks for network analysis. Most of the existing network representation learning algorithms mainly focus on the preservation of micro or macro network structure, ignoring the mesoscopic community structure information. Although a few network embedding methods are proposed to preserve the community structure, they all ignore the prior information about communities. Inspired by the semi-supervised community detection in complex networks, in this article, a novel Semi-Supervised DeepWalk method(SSDW) is proposed for network representation learning, which successfully preserves the community structure of network in the embedding space. Specifically, a semi-supervised random walk sampling method which effectively integrates the pairwise constraints is proposed. By doing so, the SSDW model can guide the transition probability in the random walk process and obtain the node context sequence in line with the prior knowledge. The experimental results on eight real networks show that comparing with the popular network embedding methods, the node representation vectors integrating pairwise constraints into the random walk process can obtain higher accuracy on node clustering task, and the results of link prediction, network visualization tasks indicate that the semi-supervised model SSDW is more discriminative than unsupervised ones.