J4 ›› 2011, Vol. 49 ›› Issue (01): 93-97.

• 计算机科学 • 上一篇    下一篇

一种新的基于社区结构的影响最大化方法

冀进朝, 黄岚, 王喆, 李红明, 李三义   

  1. 吉林大学 计算机科学与技术学院, 长春 130012
  • 收稿日期:2010-01-17 出版日期:2011-01-26 发布日期:2011-02-19
  • 通讯作者: 王喆 E-mail:wz2000@jlu.edu.cn

A New Approach to Maximizing the Spread of InfluenceBased on Community Structure

JI Jinchao, HUANG Lan, WANG Zhe, LI Hongming, LI Sanyi   

  1. College of Computer Science and Technology, Jilin University, Changchun 130012, China
  • Received:2010-01-17 Online:2011-01-26 Published:2011-02-19
  • Contact: WANG Zhe E-mail:wz2000@jlu.edu.cn

摘要:

基于传播网络的结构性, 提出一种新的基于社区结构的影响最大化方法AMICS. 该方法先利用已有社区挖掘算法识别出隐藏在网络中的社区结构, 然后迭代选择跨越社区数最多的k个节点作为影响的初始传播者最大化影响的社区覆盖. 在小型网络和中等规模网络数据集上的实验表明, 该算法比传统的影响最大化方法更具优势.

关键词: 社区结构, 影响最大化, 社区覆盖

Abstract:

Considering the structure of diffusion network, we proposed a new approach to maximizing the spread of influence based on community structure (AMICS). Our approach employs the community algorithm such as Radicchi’s algorithm/ICS algorithm to detect the community structure hidden in the network firstly, then iteratively chooses k important nodes which span the maximum communities to maximize the influence’s community coverage. The experiments on the small network and medium network show that AMICS is feasible and effective.

Key words: community structure, maximizing influence, community covered

中图分类号: 

  • TP391