Community detection in networks through a deep robust auto-encoder nonnegative matrix factorization.

Community detection is a field of research with increasing influence due to its importance in dimensionality reduction and revealing the organizational patterns in networks. A community or cluster in a network reflects the interactions among the objects that are strongly connected and distinguish them from the ones in other clusters. Over the past decades, various algorithms have been developed to detect the community structure in networks. However, the performance of these algorithm is affected by the existence of noise or nonlinear information in the network. Consequently, deep learning algorithms arise to tackle this issue. In this paper, we propose a novel deep robust auto-encoder nonnegative matrix factorization (DRANMF) approach to detect the community structure in networks. In particular, DRANMF consists of a deep structured decoder and encoder components to transform the high-order proximity matrix of the network to and from the cluster membership space, respectively. Moreover, we enhance the robustness of the proposed approach against noise and outliers by adopting the l 2 , 1 -norm to formulate the objective function. The experiments on multiple synthetic and real-world networks demonstrate that the proposed DRANMF has a better performance and robustness than other existing methods. [ABSTRACT FROM AUTHOR]