Weighted compactness function based label propagation algorithm for community detection

Community detection in complex networks, is to detect the community structure with the internal structure relatively compact and the external structure relatively sparse, according to the topological relationship among nodes in the network. In this paper, we propose a compactness function which combines the weight of nodes, and use it as the objective function to carry out the node label propagation. Firstly, according to the node degree, we find the sets of core nodes which have great influence on the network. The more the connections between the core nodes and the other nodes are, the larger the amount of the information these kernel nodes receive and transform. Then, according to the similarity of the nodes between the core nodes sets and the nodes degree, we assign weights to the nodes in the network. So the label of the nodes with great influence will be the priority in the label propagation process, which effectively improves the accuracy of the label propagation. The compactness function between nodes and communities in this paper is based on the nodes influence. It combines the connections between nodes and communities with the degree of the node belongs to its neighbor communities based on calculating the node weight. The function effectively uses the information of nodes and connections in the network. The experimental results show that the proposed algorithm can achieve good results in the artificial network and large-scale real networks compared with the 8 contrast algorithms.