  Understanding community structure in social media is critical due to its
broad applications such as friend recommendations, link predictions and
collaborative filtering. However, there is no widely accepted definition of
community in literature. Existing work use structure related metrics such as
modularity and function related metrics such as ground truth to measure the
performance of community detection algorithms, while ignoring an important
metric, size of the community. [1] suggests that the size of community with
strong ties in social media should be limited to 150. As we discovered in this
paper, the majority of the communities obtained by many popular community
detection algorithms are either very small or very large. Too small communities
don't have practical value and too large communities contain weak connections
therefore not stable. In this paper, we compare various community detection
algorithms considering the following metrics: size of the communities, coverage
of the communities, extended modularity, triangle participation ratio, and user
interest in the same community. We also propose a simple clique based algorithm
for community detection as a baseline for the comparison. Experimental results
show that both our proposed algorithm and the well-accepted disjoint algorithm
InfoMap perform well in all the metrics.
