计算机科学 ›› 2017, Vol. 44 ›› Issue (12): 28-32.doi: 10.11896/j.issn.1002-137X.2017.12.005

• 第四届CCF大数据学术会议 • 上一篇    下一篇

一种面向主题耦合的影响力最大化算法

吕文渊,周丽华,廖仁建   

  1. 云南大学信息学院 昆明650000,云南大学信息学院 昆明650000,云南大学信息学院 昆明650000
  • 出版日期:2018-12-01 发布日期:2018-12-01
  • 基金资助:
    本文受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

Coupled Topic-oriented Influence Maximization Algorithm

LV Wen-yuan, ZHOU Li-hua and LIAO Ren-jian   

  • Online:2018-12-01 Published:2018-12-01

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。

关键词: 社会网络,影响力最大化,耦合相似度,主题

Abstract: As networks are main tool for communication in the modern society,digging the most influential network users has become a hot issue.This study proposed a coupled topic-oriented influence maximization algorithm.It analyzes couplings among topics and extends the independent cascade model by considering couple similarity among topics and users’ preference on different topics.The classical greedy algorithm is used to dig the most influential users on the extended model.Compared with the influence maximization algorithm without the coupled topic,the proposed algorithm digs out more rational users who can affect more users in networks.

Key words: Social network,Influence maximization,Similarity of coupling,Topic

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