A community detecting algorithm based on local information

Detecting community structure is an important task in researches of complex networks and methods depend only on topological information are of great practical value. Existing algorithms based on global quantities are usually with high level of time consumption. In this paper, we propose a divisive method to discover the community structure based on a local quantity in order to improve the computational efficiency. To demonstrate the effectiveness of this algorithm, experiments are taken on both computer-generated graphs and real-world networks. The result shows that our algorithm is much faster, and has the same level of accuracy with existing algorithm.