Network Embedding Using Semi-Supervised Kernel Nonnegative Matrix Factorization

Abstract

Network embedding, aiming to learn low-dimensional representations of nodes in networks, is very useful for many vector-based machine learning algorithms and has become a hot research topic in network analysis. Although many methods for network embedding have been proposed before, most of them are unsupervised, which ignores the role of prior information available in the network. In this paper, we propose a novel method for network embedding using semi-supervised kernel nonnegative matrix factorization (SSKNMF), which can incorporate prior information and thus to learn more useful features from the network through introducing kernel methodology. Besides, it can improve robustness against noises by using the objective function based on L₂,1 norm. Efficient iterative update rules are derived to resolve the network embedding model using the SSKNMF, and the convergence of these rules are strictly proved from the perspective of mathematics. The results from extensive experiments on several real-world networks show that our proposed algorithm is effective and has better performance than the existing representative methods.