Hub search method based on sampling

Abstract

Hubs play important roles in scale-free networks. Existing hub search algorithms mostly assume the availability of the global network structure and use a variety of centrality metrics to search the hubs in the network. However, when it is very difficult to obtain the network topology in large-scale networks, how we can search the hubs? In this paper, a hub search method based on sampling with biased algorithms is proposed and further four algorithms are compared, including improved MHRW (Metropolis-Hasting Random Walk), MDF (Maximum-Degree First), BFS (Breadth-First Search) and RW (Random Walk). The experiments on several datasets show that both MDF and improved MHRW algorithm can reach a higher HDR (Hub Detection Rate) than BFS and RW, and when the sampling rate goes above 10%, MDF and improved MHRW can find an average of more than 70% of hubs in scale-free network.