Earticle

Label propagation algorithm (LPA algorithm) is gotten widely attention by its simplicity, rapidity and greater effectiveness. Aimed at the problem of label updating process is sensitive to the order of nodes in LPA algorithm, an improved algorithm is proposed in this paper. The algorithm starts from the nodes with maximum degree in the network, according to its neighbor nodes’ community similarity index to decide whether its label setting, and then completes label setting in the first round; On this basis, it continues to carry out the iteration in label propagation algorithm, completes the entire network of community structure detection. The experimental results show that the improved algorithm is slightly better than the original algorithm at running time and number of iterations, has higher robustness, prevents the occurrence of the trivial solution.

According to the defects that community detection algorithm in unknown complex networks has a pre-parameter. We propose Adaptive Label Propagation Algorithm (ALPA) to detect community structures in complex networks. The ALPA algorithm find out all disjoint Maximal Clique (MC) and let each MC share the identical weight and unique label so as to reduce the redundant labels and uncontrollable factors. The stability of ALPA algorithm is enhanced by synchronous update during iterations. Meanwhile it will converge easily due to the termination condition that all of the vertexes have the label. During iterations we use the adaptive threshold method to overcome the pre-parameter limitation. Compared with other community detection algorithms in synthetic networks and real networks, our experiments show that ALPA algorithm not only improves the tolerance of mixing parameter, but also enhances its robustness.