Abstract: Community detecting has been a research topic in the complex network area. The global information of the whole network, which is rectuired by the traditional community detecting algorithms, is hard to get when the scale of the network grows. On the other hand, in many cases we only care about the local community of one particular node of the network. To make the local community detecting faster and more accurate, this paper proposed a local community detecting method based on the clustering coefficient of the nodes. The proposed method, which leverages the connectivity density and the characteristics of clustering coefficient, starts from the target node of the network and detects the community it belongs to by searching the neighbor nodes. This method rectuires only the local network information related to the target node and is faster compared to the traditional community detecting algorithm. It is also applicable for global community structure detecting. I}he method was applied to the Zachary network and JSCG, and the experiment results were analyzed by comparing with the actual characteristics of the object network.

Key words: Community, Clustering coefficient, Community detecting