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Compressive sensing of high betweenness centrality nodes in networks

Hamidreza Mahyar, Rouzbeh Hasheminezhad, Elahe Ghalebi K., Ali Nazemian, Radu Grosu, Ali Movaghar and Hamid R. Rabiee

Physica A: Statistical Mechanics and its Applications, 2018, vol. 497, issue C, 166-184

Abstract: Betweenness centrality is a prominent centrality measure expressing importance of a node within a network, in terms of the fraction of shortest paths passing through that node. Nodes with high betweenness centrality have significant impacts on the spread of influence and idea in social networks, the user activity in mobile phone networks, the contagion process in biological networks, and the bottlenecks in communication networks. Thus, identifying k-highest betweenness centrality nodes in networks will be of great interest in many applications. In this paper, we introduce CS-HiBet, a new method to efficiently detect top-k betweenness centrality nodes in networks, using compressive sensing. CS-HiBet can perform as a distributed algorithm by using only the local information at each node. Hence, it is applicable to large real-world and unknown networks in which the global approaches are usually unrealizable. The performance of the proposed method is evaluated by extensive simulations on several synthetic and real-world networks. The experimental results demonstrate that CS-HiBet outperforms the best existing methods with notable improvements.

Keywords: Compressive sensing; Betweenness centrality; Complex network (search for similar items in EconPapers)
Date: 2018
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Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

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