  Community identification is a long-standing challenge in the modern network
science, especially for very large scale networks containing millions of nodes.
In this paper, we propose a new metric to quantify the structural similarity
between subgraphs, based on which an algorithm for community identification is
designed. Extensive empirical results on several real networks from disparate
fields has demonstrated that the present algorithm can provide the same level
of reliability, measure by modularity, while takes much shorter time than the
well-known fast algorithm proposed by Clauset, Newman and Moore (CNM). We
further propose a hybrid algorithm that can simultaneously enhance modularity
and save computational time compared with the CNM algorithm.
