Least cost influence propagation in (social) networks

被引:0
|
作者
Matteo Fischetti
Michael Kahr
Markus Leitner
Michele Monaci
Mario Ruthmair
机构
[1] University of Padua,DEI
[2] University of Vienna,Department of Statistics and Operations Research
[3] University of Bologna,DEI
来源
Mathematical Programming | 2018年 / 170卷
关键词
Influence maximization; Mixed-integer programming; Social network analysis; 90B10; 90C11; 90C27;
D O I
暂无
中图分类号
学科分类号
摘要
Influence maximization problems aim to identify key players in (social) networks and are typically motivated from viral marketing. In this work, we introduce and study the Generalized Least Cost Influence Problem (GLCIP) that generalizes many previously considered problem variants and allows to overcome some of their limitations. A formulation that is based on the concept of activation functions is proposed together with strengthening inequalities. Exact and heuristic solution methods are developed and compared for the new problem. Our computational results also show that our approaches outperform the state-of-the-art on relevant, special cases of the GLCIP.
引用
收藏
页码:293 / 325
页数:32
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