A algorithm based on the local module degree for community detection in complex networks

Community structure is a common property that exists in complex networks. This paper presents a new method which can detect community structure based on the idea of local modularity measure. The algorithm firstly starts from the node which has the max Multifesture of nodes, and finds the candidate node from the candidate set which can reach the maximum of the local modularity measure Q. Secondly, the algorithm merge the node into the community and update the candidate set. At last, clustering results can be received. Since this algorithm only requires local information of the complex network, its time complexity is very low. It can find clustering centers better based on the multifesture value of nodes. Finally, this algorithm is applied to a classical social network, the Zachary network, with satisfactory result, the experiment shows the validity of this method.