  Understanding community structure of social media is critical due to its
broad applications such as friend recommendations, user modeling and content
personalizations. Existing research uses structural metrics such as modularity
and conductance and functional metrics such as ground truth to measure the
qualify of the communities discovered by various community detection
algorithms, while overlooking a natural and important dimension, community
size. Recently, anthropologist Dunbar suggests that the size of stable
community in social media should be limited to 150, referred to as Dunbar's
number. In this study, we propose a systematic way of algorithm comparison by
orthogonally integrating community size as a new dimension into existing
structural metrics for consistently and holistically evaluating the community
quality in social media context. we design a heuristic clique based algorithm
which controls the size and overlap of communities with adjustable parameters
and evaluate it along with five state-of-the-art community detection algorithms
on both Twitter network and DBLP network. Specifically, we divide the
discovered communities based on their size into four classes called close
friend, casual friend, acquaintance, and just a face, and then calculate the
coverage, modularity, triangle participation ratio, conductance, transitivity,
and the internal density of communities in each class. We discover that
communities in different classes exhibit diverse structural qualities and many
existing community detection algorithms tend to output extremely large
communities.
