Identifying functional modules in complex networks

In this paper, we propose a new method that enables us to detect and describe the functional modules in complex networks. Using the proposed method, we call classify the nodes of networks into different modules according to their pattern of intra- and extra-module links. We use our method to analyze the modular structures of the ER random networks. We find that different modules of networks have different structure properties, such as the clustering coefficient. Moreover, at the same time, many nodes of networks participate different modules. Remarkably, we find that in the ER random networks, when the probability p is small, different modules or different roles of nodes call be identified by different regions in the c-p parameter space.