一种面向主题耦合的影响力最大化算法
发布时间：2019-02-14 11:38
【摘要】：网络逐渐成为了人与人之间的主要社交工具,在网络中挖掘最有影响力的用户成为了非常值得关注的问题。在传统影响力最大化算法的基础上提出了一种面向主题耦合的影响力最大化算法,该算法首先分析网络中不同主题之间的耦合相似性,在综合考虑主题之间耦合相似性与用户对不同主题偏好的基础上扩展独立级联模型,并使用经典的贪心算法挖掘最具有影响力的用户。与不考虑主题耦合的影响力最大化算法相比,所提算法考虑了传播主题之间的耦合相似性,并且能够与用户偏好进行更为有效地结合。最后,实验表明,相比于经典的影响力最大化算法,该算法能够更为有效地挖掘在特定主题下最具有影响力的种子节点。
[Abstract]:The Internet has gradually become the main social tool between people, mining the most influential users in the network has become a very important issue. Based on the traditional influence maximization algorithm, a topic coupling oriented influence maximization algorithm is proposed. Firstly, the coupling similarity between different topics in the network is analyzed. On the basis of considering the coupling similarity between topics and users' preference for different topics, the independent cascade model is extended, and the most influential users are mined by the classical greedy algorithm. Compared with the influence maximization algorithm which does not consider the topic coupling, the proposed algorithm takes into account the coupling similarity between propagating topics and is more effective in combining with user preferences. Finally, experiments show that the algorithm can effectively mine the most influential seed nodes under a particular topic compared with the classical algorithm.
【作者单位】： 云南大学信息学院;
【基金】：国家自然科学基金项目(61262069,61472346,61762090) 云南省自然科学基金项目(2015FB114,2016FA026) 云南省创新团队 云南省高校科技创新团队(IRTSTYN) 云南大学创新团队发展计划(XT412011)资助
【分类号】：TP301.6
，
本文编号：2422173
[Abstract]:The Internet has gradually become the main social tool between people, mining the most influential users in the network has become a very important issue. Based on the traditional influence maximization algorithm, a topic coupling oriented influence maximization algorithm is proposed. Firstly, the coupling similarity between different topics in the network is analyzed. On the basis of considering the coupling similarity between topics and users' preference for different topics, the independent cascade model is extended, and the most influential users are mined by the classical greedy algorithm. Compared with the influence maximization algorithm which does not consider the topic coupling, the proposed algorithm takes into account the coupling similarity between propagating topics and is more effective in combining with user preferences. Finally, experiments show that the algorithm can effectively mine the most influential seed nodes under a particular topic compared with the classical algorithm.
【作者单位】： 云南大学信息学院;
【基金】：国家自然科学基金项目(61262069,61472346,61762090) 云南省自然科学基金项目(2015FB114,2016FA026) 云南省创新团队 云南省高校科技创新团队(IRTSTYN) 云南大学创新团队发展计划(XT412011)资助
【分类号】：TP301.6
，
本文编号：2422173
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