Complex networks display community structures. Nodes within groups are densely connected but among groups are sparsely connected. In this paper, an algorithm is presented for community detection named Markov Random Wa...Complex networks display community structures. Nodes within groups are densely connected but among groups are sparsely connected. In this paper, an algorithm is presented for community detection named Markov Random Walks Ants(MRWA). The algorithm is inspired by Markov random walks model theory, and the probability of ants located in any node within a cluster will be greater than that located outside the cluster.Through the random walks, the network structure is revealed. The algorithm is a stochastic method which uses the information collected during the traverses of the ants in the network. The algorithm is validated on different datasets including computer-generated networks and real-world networks. The outcome shows the algorithm performs moderately quickly when providing an acceptable time complexity and its result appears good in practice.展开更多
基金the National High Technology Research and Development(863)Program of China(No.2015AA043701)
文摘Complex networks display community structures. Nodes within groups are densely connected but among groups are sparsely connected. In this paper, an algorithm is presented for community detection named Markov Random Walks Ants(MRWA). The algorithm is inspired by Markov random walks model theory, and the probability of ants located in any node within a cluster will be greater than that located outside the cluster.Through the random walks, the network structure is revealed. The algorithm is a stochastic method which uses the information collected during the traverses of the ants in the network. The algorithm is validated on different datasets including computer-generated networks and real-world networks. The outcome shows the algorithm performs moderately quickly when providing an acceptable time complexity and its result appears good in practice.
