Overlap between community pairs is commonplace in large-scale social networks.The most existing overlapped community detection algorithms may falsely identify overlaps as communities because the overlap area is denser than others

and those algorithms are computationally demanding and cannot scale well with the size of networks.In this paper

we propose a fast overlapping community discovery algorithm based on some locally computed information-Local information based Fast Overlapped Communities Detection (Li-FOCD).Firstly

we introduce two local information metrics for each network node-community connectivity score and neighborho

od connectivity score

to model the relationship between nodes and communities;secondly

based on local metrics

we can concurrently execute the iterations of reduction

expansion

and duplication removal to find the approximately optimal communities instead of the optimal community

and achieve a low complexity

O

(

m

+

n

).Experimental analysis based on real large-scale social networking datasets shows that our algorithm outperforms some popular overlapped community finding algorithms in terms of computational time while not compromising with quality.