An adaptive population control framework for ACO-based community detection

The community structure is one of the most important features of complex networks and has wide research and application prospects. To find the community structure, many researchers currently focus on natural heuristic methods, where an extraordinary swarm intelligence algorithm (i.e., the ant colony algorithm) is widely adopted to detect the potential community structures. However, the computational cost of such an algorithm is so high that it restricts the property and range of application. In this paper, we present a novel adaptive population control framework for ACO-based community discovery approaches to overcome the mentioned shortcomings. Specifically, this framework dynamically controls the number of ants based on the slope of the modularity and iterations. Such a framework is adopted in two different algorithms and we make corresponding comparison between this one and traditional ACO-based algorithms in six classical real networks and five synthetic datasets. Experiments show that ant colony algorithms with our proposed framework have evidently reduced time complexity and maintained the quality of community structure simultaneously.