Article ID: | iaor201526109 |
Volume: | 30 |
Issue: | 1 |
Start Page Number: | 97 |
End Page Number: | 108 |
Publication Date: | Jul 2015 |
Journal: | Journal of Combinatorial Optimization |
Authors: | Jiang Yiwei, Wu Weili, Wu Lidong, Zhu Yuqing, Bi Yuanjun, Xu Wen |
Keywords: | networks, internet |
Influence maximization is a classic and hot topic in social networks. In this paper, firstly we argue that in online social networks, due to the time sensitivity of popular topics, the assumption in IC or LT model that the influence propagates endlessly in the network, is not applicable. Based on this we consider influence transitivity and limited propagation distance in our new model. Secondly, under our model we propose Semidefinite based algorithms. While most existing algorithms rely on monotony and submodularity to obtain approximation ratio of