A Local Strengthened Multi-label Propagation Algorithm for Community Detection
MA Qian-l · Jisuanji gongcheng · 2014
In the social networks,community and circle are groups of vertices with relatively dense intra-connection,but the circle is of small-scale.Intuitively,circles are important local information and community detection can benefit from them.Unfortunately,in most existing label propagation methods for community detection,the circle-based information is not taken into account.Aiming at this problem,this paper proposes a Local Strengthened Multi-label Propagation(LSMLP) algorithm for community detection.It first gives the definition of circle and then proposes an iterative strategy for multi-label propagation by using circle-based information.Based on a modularity optimization,a unique label can be selected from multi-labels.Performance properties of the LSMLP are discussed and compared with some related methods on several real networks.The method is more highly efficient and effective for uncovering communities.