Abstract
The study of community detection algorithms in complex networks has been very active in the past several years. In this paper, a Hybrid Self-adaptive Community Detection Algorithm (HSCDA) based on modularity is put forward first. In HSCDA, three different crossover and two different mutation operators for community detection are designed and then combined to form a strategy pool, in which the strategies will be selected probabilistically based on statistical self-adaptive learning framework. Then, by adopting the best evolving strategy in HSCDA, a Multiobjective Community Detection Algorithm (MCDA) based on kernelk-means (KKM) and ratio cut (RC) objective functions is proposed which efficiently make use of recommendation of strategy by statistical self-adaptive learning framework, thus assisting the process of community detection. Experimental results on artificial and real networks show that the proposed algorithms achieve a better performance compared with similar state-of-the-art approaches.
Highlights
Since many complex systems, such as the Internet, social networks, and biological networks, can be modeled as complex networks, the study of complex networks is essential to better understand and analyze such systems
To further improve the solution quality of intelligent optimization algorithms for community detection, Hybrid Self-adaptive Community Detection Algorithm (HSCDA) and Multiobjective Community Detection Algorithm (MCDA) are proposed based on evolutionary algorithm, respectively
In HSCDA, Q is set as the objective function and six different evolution strategies are designed to construct hybrid evolution strategy pool
Summary
Since many complex systems, such as the Internet, social networks, and biological networks, can be modeled as complex networks, the study of complex networks is essential to better understand and analyze such systems. As one of the most popular methods, the modularity-based methods have attracted many researchers’ attention, with the most characteristic feature of converting the network clustering problem into an optimization problem by maximizing the modularity Q presented by Girvan and Newman [6]. The above work shows that the modularity-based intelligent optimization algorithms for community detection attract much attention of researchers. In order to further improve the performance of intelligent optimization algorithms for community detection, the paper proposes a new framework including hybrid evolving strategies and adaptive learning mechanism based on evolutionary algorithm. A Multiobjective Community Detection Algorithm (MCDA) is proposed, in which KKM and RC are used as two optimization objectives instead of the modularity. Experimental results show that MCDA achieves a better performance compared with HSCDA and other multiobjective based algorithms, MOGA-net, MOCD, and MOEA/D-net.
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