Abstract
Currently, many community detection methods are proposed in the network science field. However, most contemporary methods only employ modularity to detect communities, which may not be adequate to represent the real community structure of networks for its resolution limit problem. In order to resolve this problem, we put forward a new community detection approach based on a differential evolution algorithm (CDDEA), taking into account modularity density as an optimized function. In the CDDEA, a new tuning parameter is used to recognize different communities. The experimental results on synthetic and real-world networks show that the proposed algorithm provides an effective method in discovering community structure in complex networks.
Highlights
An increasing number of researchers are paying attention to the community structure identification [1,2,3] of networks, including social networks [4,5,6,7], biology networks [6,8], worldwide web networks [9,10], and so on, in order to have a profound understanding of their structures and functions
Since modularity density was proven to resolve the resolution limit problem of the modularity function, we introduce it in our paper and propose a community detection algorithm based on a differential evolution algorithm using modularity density
Our proposed algorithm CDDEA was implemented in MATLAB R2012b, and all experiments were conducted on Windows 7 with an Intel(R) Core (TM) i5-2520M processor, 2.5 GHz, 4 Gb RAM
Summary
An increasing number of researchers are paying attention to the community structure identification [1,2,3] of networks, including social networks [4,5,6,7], biology networks [6,8], worldwide web networks [9,10], and so on, in order to have a profound understanding of their structures and functions. One of most famous methods, called network modularity Q [13], is used as a quality metric for measuring the partition of networks According to this measurement, many successful algorithms were proposed by optimizing the modularity Q function, such as those described in References [2,5,14,15,16]. Proposed a new quality function named modularity density, and demonstrated its superiority in detecting communities compared to traditional modularity-based methods. Since modularity density was proven to resolve the resolution limit problem of the modularity function, we introduce it in our paper and propose a community detection algorithm based on a differential evolution algorithm using modularity density. We put forward a community detection method based on the differential evolution algorithm (CDDEA), which adopts modularity density as an optimization function and can explore the network in different resolutions.
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