Community detection method based on mixed-norm sparse subspace clustering

Community or group is an important structure in disciplines such as social networks, biology gene expression, and physics systems. Community detections for different types of networks have attracted considerable interest. However, it is still challenging to find meaningful community structures in various networks. In particular, accurate community description and implementation of effective detection algorithms with huge datasets are still not solved. In this paper, we present a novel community detection algorithm based on the theory of sparse subspace clustering (SSC) with mixed-norm constraints. Inspired by the sparse subspace representation theory, each community in a given network can span a subspace in some similarity measure space. If the basis of subspaces can be solved, all of the nodes can be represented as a linear combination of the nodes that span the same subspace. By introducing a novel mixed-norm constraint in SCC, the connections of nodes among different communities are modeled as noise to improve the clustering accuracy. The formulation of the basis of subspaces is derived from the self-representation property of data by using SSC. Then, the alternating directions method of multipliers (ADMM) framework is used to solve the formulation. Finally, communities are detected by spectral clustering method. The proposed method is compared with the state-of-the-art algorithms on synthetic networks and real-world networks. Experimental results show the effectiveness of the proposed method in accurately describing the community. The results also show that the mixed-norm SSC is a practical approach for detecting communities with huge datasets. (C) 2017 Published by Elsevier B.V.