  The growing popularity of online social networks has provided researchers
with access to large amount of social network data. This, coupled with the ever
increasing computation speed, storage capacity and data mining capabilities,
led to the renewal of interest in automatic community detection methods.
Surprisingly, there is no universally accepted definition of the community. One
frequently used definition states that ``communities, that have more and/or
better-connected `internal edges' connecting members of the set than `cut
edges' connecting the set to the rest of the world''[Leskovec et al. 20008].
This definition inspired the modularity-maximization class of community
detection algorithms, which look for regions of the network that have higher
than expected density of edges within them. We introduce an alternative
definition which states that a community is composed of individuals who have
more influence on others within the community than on those outside of it. We
present a mathematical formulation of influence, define an influence-based
modularity metric, and show how to use it to partition the network into
communities. We evaluated our approach on the standard data sets used in
literature, and found that it often outperforms the edge-based modularity
algorithm.
