Large-scale topic community mining based on distributed nonnegative matrix factorization

Abstract

Mining topic community in complex networks is of great applicable value. However, most of the existing methods cannot effectively mine topic community in large-scale complex networks because of their weak scalabilities. To rectify this problem, we propose a method called TCMDNMF that is based on the joint nonnegative matrix factorization model. The proposed method can effectively integrate node link and content information to mine topic community. We adopt the gradient descent method as the optimized solution to the topic community mining model. Further, to improve the computing efficiency of TCMDNMF, we use $L_1$ norm as the sparsity regularization term and implement the key algorithms based on the MapReduce distributed computing framework. The results of extensive experiments conducted demonstrate that our method is effective and is highly scalable. Furthermore, it very effectively solves the problem of processing large volumes of data brought by topic community mining in large-scale complex networks.