Hub search method based on sampling

Author

Zhefeng Xiao ; Bo Liu ; Huaping Hu ; Tianzuo Wang

Author_Institution

Sch. of Comput. Sci., Nat. Univ. of Defense Technol., Changsha, China

fYear

2014

fDate

9-11 June 2014

Firstpage

2011

Lastpage

2014

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.

Keywords

network theory (graphs); sampling methods; search problems; BFS; HDR; MDF; MHRW; Metropolis-Hasting random walk; RW; biased algorithms; breadth-first search; centrality metrics; hub detection rate; hub search algorithms; improved MHRW; large-scale networks; maximum-degree first; network topology; sampling; scale-free networks; Algorithm design and analysis; Computer science; Educational institutions; Measurement; Network topology; Search methods; Social network services; Hubs; Search; scale-free network;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Electronics and Applications (ICIEA), 2014 IEEE 9th Conference on

Conference_Location

Hangzhou

Print_ISBN

978-1-4799-4316-6

Type

conf

DOI

10.1109/ICIEA.2014.6931499

Filename

6931499