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Efficient collective influence maximization in cascading processes ​with first-order transitions

2017年06月07日 10:47  点击:[]

报告题目:Efficient collective influence maximization in cascading processes with first-order transitions

报告人: Sen Pei     Columbia University

报告时间:20176 16日(星期五)下午 14:00-15:00

报告地点:创新园大厦 A1101

校内联系人:张仁权      联系电话:84708351-8123

 

Abstract: In many social and biological networks, the collective dynamics of the entire system can be shaped by a small set of influential units through a global cascading process, manifested by an abrupt first-order transition in dynamical behaviors. Despite its importance in applications, efficient identification of multiple influential spreaders in cascading processes still remains a challenging task for large-scale networks. Here we address this issue by exploring the collective influence in general threshold models of cascading process. Our analysis reveals that the importance of spreaders is fixed by the subcritical paths along which cascades propagate: the number of subcritical paths attached to each spreader determines its contribution to global cascades. The concept of subcritical path allows us to introduce a scalable algorithm for massively large-scale networks. Results in both synthetic random graphs and real networks show that the proposed method can achieve larger collective influence given the same number of seeds compared with other scalable heuristic approaches.

 

报告人介绍:裴森,美国哥伦比亚大学公共卫生学院博士后研究员20157月毕业于北京航空航天大学数学与系统科学学院,获博士学位。主要从事复杂网络上的扩散行为、疾病传播等方面的研究,尤其是扩散过程中的临界现象研究。

 

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