Abstract
Community detection in dynamic networks is an important research topic and has received an enormous amount of attention in recent years. Modularity is selected as a measure to quantify the quality of the community partition in previous detection methods. But, the modularity has been exposed to resolution limits. In this paper, we propose a novel multiobjective evolutionary algorithm for dynamic networks community detection based on the framework of nondominated sorting genetic algorithm. Modularity density which can address the limitations of modularity function is adopted to measure the snapshot cost, and normalized mutual information is selected to measure temporal cost, respectively. The characteristics knowledge of the problem is used in designing the genetic operators. Furthermore, a local search operator was designed, which can improve the effectiveness and efficiency of community detection. Experimental studies based on synthetic datasets show that the proposed algorithm can obtain better performance than the compared algorithms.
Highlights
Many real-world complex systems take the form of networks
We propose a novel multiobjective evolutionary algorithm for dynamic networks community detection based on the framework of nondominated sorting genetic algorithm
Dynamic networks capture the modifications of interconnections over time, which allow tracing the changes of network structure at different time steps
Summary
Many real-world complex systems take the form of networks. Acquaintance networks, Internet, power grids, and neural networks are some examples. In order to smooth each community over time, it needs to trade off two competing objectives of snapshot quality, which is that the clustering should reflect as accurately as possible the data coming during the current time step, and temporal cost, which is that each clustering should not shift dramatically from one time step to the successive one Using this idea, Folino and Pizzuti proposed a multiobjective approach named DYN-MOGA to discover communities in dynamic networks by employing genetic algorithms [3]. Mathematical Problems in Engineering work, Gong et al introduced a novel multiobjective immune algorithm with local search to solve the community detection problem in dynamic networks [4] They adopted Modularity and NMI as two objectives to optimize.
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