@article {IOPORT.05832980,
    author       = {Yu, Liang and Gao, Lin and Wang, Danyang and Fu, Shaofeng},
    title        = {Quantitative function for community structure detection.},
    year         = {2010},
    journal      = {International Journal of Data Mining, Modelling and Management (IJDMMM)},
    volume       = {2},
    number       = {4},
    issn         = {1759-1163},
    pages        = {351-368},
    publisher    = {Inderscience Publishers, Gen\`eve},
    doi          = {10.1504/IJDMMM.2010.035563},
    abstract     = {Summary: Detecting community structure is a powerful approach to understanding complex networks. Recently, the modularity function $Q$ has been widely used as a measure to identify communities in complex networks. However, optimising $Q$ has some resolution limitations. In this paper, we present a new quantitative function $DQ$ (degree modularity) that detects community structures 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.},
    identifier   = {05832980},
}