Low-rank subspace learning based network community detection

Community structure is one of the most important inherent properties in complex networks. Generally, nodes in each community span a different subspace in the geometric space. However, the subspace based methods are not well studied, and the existing related algorithms are sensitive to the perturbations in networks, thus lack the ability to capture the robust subspace segmentation accurately. In this paper we propose a low-rank subspace learning based network community detection algorithm. The contribution of this paper is to find the lowest rank representation of all node vectors jointly in the geometric space using low-rank decomposition strategy. Then the final robust low-rank subspace based communities are obtained from the lowest rank representations. Low-rank decomposition is better at capturing global structure of networks, thus our algorithm is more robust to perturbations in networks and has a better performance on discriminating the community boundaries. Experimental results on both synthetic benchmarks and real-world networks demonstrate that the proposed algorithm outperforms the state-of-the-art community detection approaches and the results basically preserve the actual community structure, especially on networks with vague community structures.