A discrete particle swarm optimization box-covering algorithm for fractal dimension on complex networks

Li Kuang, Feng Wang, Yuanxiang Li, Haiqiang Mao, Min Lin, Fei Yu

Abstract: Researchers have widely investigated the fractal property of complex networks, in which the fractal dimension is normally evaluated by box-covering method. The crux of box-covering method is to find the solution with minimum number of boxes to tile the whole network. Here, we introduce a particle swarm optimization box-covering (PSOBC) algorithm based on discrete framework. Compared with our former algorithm, the new algorithm can map the search space from continuous to discrete one, and reduce the time complexity significantly. Moreover, because many real-world networks are weighted networks, we also extend our approach to weighted networks, which makes the algorithm more useful on practice. Experiment results on multiple benchmark networks compared with state-of-the-art algorithms show that this PSOBC algorithm is effective and promising on various network structures.