Negative-Aware Influence Maximization on Social Networks

被引：0
|
作者：
Chen, Yipeng [1 ]
Li, Hongyan [1 ]
Qu, Qiang [1 ,2 ]
机构：
[1] Peking Univ, Sch Elect Engn & Comp Sci, MOE Key Lab Machine Percept, Beijing, Peoples R China
[2] Chinese Acad Sci, Shenzhen Inst Adv Technol, Beijing, Peoples R China
来源：
THIRTY-SECOND AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTIETH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / EIGHTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE | 2018年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
How to minimize the impact of negative users within the maximal set of influenced users? The Influenced Maximization (IM) is important for various applications. However, few studies consider the negative impact of some of the influenced users. We propose a negative-aware influence maximization problem by considering users' negative impact. A novel algorithm is proposed to solve the problem. Experiments on real-world datasets show the proposed algorithm can achieve 70% improvement on average in expected influence compared with rivals.
引用
收藏
页码：8063 / 8064
页数：2
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