Quantitative function for community structure detection

Detecting community structure is a powerful approach to understanding complex networks. Recently, modularity function Q has been widely used as a measure to identify communities in complex networks. However, optimising Q function has some resolution limitations. In this paper, we present a new quantitative function DQ (degree modularity) that detects community structure based on local connectivity of communities. We first prove that the function DQ can improve the resolution limitations of modularity Q. Furthermore, we experimentally evaluate the performance of the new quantitative function using a variety of real and computer-generated networks and find communities of widely differing sizes can be detected with higher sensitivity and reliability. Also, even in large-scale biological networks, such as protein-protein interaction (PPI) networks, we can obtain higher matching rate between the predicted protein modules and the known protein complexes. All the experimental results support the usefulness of the new quantitative function DQ as the measure for community structure detection.