Minimizing the expected complete influence time of a social network

Authors:
Yaodong Ni;Lei Xie;Zhi-Qiang Liu
Affiliations:
School of Information Technology and Management, University of International Business and Economics, Beijing, China and School of Creative Media, City University of Hong Kong, Hong Kong, China;School of Computer Science, Northwestern Polytechnical University, Xi'an, China;School of Creative Media, City University of Hong Kong, Hong Kong, China
Venue:
Information Sciences: an International Journal
Year:
2010

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Abstract

Complete influence time specifies how long it takes to influence all individuals in a social network, which plays an important role in many real-life applications. In this paper, we study the problem of minimizing the expected complete influence time of social networks. We propose the incremental chance model to characterize the diffusion of influence, which is progressive and able to achieve complete influence. Theoretical properties of the expected complete influence time under the incremental chance model are presented. In order to trade off between optimality and complexity, we design a framework of greedy algorithms. Finally, we carry out experiments to show the effectiveness of the proposed algorithms.