Article ID Journal Published Year Pages File Type
394449 Information Sciences 2010 14 Pages PDF
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.

Related Topics
Physical Sciences and Engineering Computer Science Artificial Intelligence
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