Clique-based locally consistent latent space clustering for community detection

Abstract

Community structure is one of the most important properties of complex networks and a keypoint to understanding and exploring real-world networks. One popular technique for community detection is matrix-based algorithms. However, existing matrix-based community detection models, such as nonnegative matrix factorization, spectral clustering and their variants, fit the data in a Euclidean space and have ignored the local consistency information which is crucial when discovering communities. In this paper, we propose a novel framework of latent space clustering to cope with community detection, by incorporating the clique-based locally consistency into the original objective functions to penalize the latent space dissimilarity of the nodes within the clique. We evaluate the proposed methods on both synthetic and real-world networks and experimental results show that our approaches significantly improve the accuracy of community detection and outperform state-ofthe- art methods, especially on networks with unclear structures.