  Mining community structures from the complex network is an important problem
across a variety of fields. Many existing community detection methods detect
communities through optimizing a community evaluation function. However, most
of these functions even have high values on random graphs and may fail to
detect small communities in the large-scale network (the so-called resolution
limit problem). In this paper, we introduce two novel node-centric community
evaluation functions by connecting correlation analysis with community
detection. We will further show that the correlation analysis can provide a
novel theoretical framework which unifies some existing evaluation functions in
the context of a correlation-based optimization problem. In this framework, we
can mitigate the resolution limit problem and eliminate the influence of random
fluctuations by selecting the right correlation function. Furthermore, we
introduce three key properties used in mining association rule into the context
of community detection to help us choose the appropriate correlation function.
Based on our introduced correlation functions, we propose a community detection
algorithm called CBCD. Our proposed algorithm outperforms existing
state-of-the-art algorithms on both synthetic benchmark networks and real-world
networks.
