基于亲和传播的动态社会网络影响力扩散模型  

Influence diffusion model based on affinity of dynamic social network

作　　者：陈云芳[1] 夏涛[1,2] 张伟[1] 李晋[3] 

机构地区：[1]南京邮电大学计算机学院,江苏南京210003 [2]中国电信济宁分公司,山东济宁272000 [3]北京信息科技大学公共管理与传媒学院,北京100192

出　　处：《通信学报》2016年第10期40-47,共8页Journal on Communications

基　　金：国家自然科学基金资助项目(No.61272422);北京市教育委员会人文社会科学研究计划面上基金资助项目(No.SM201411232005)~~

摘　　要：影响力最大化模型研究是近来社会网络的一个热点问题,然而传统的独立级联模型以静态网络中为基础,且激活概率一般设定为固定值。提出一种加入衰减因数的动态社会网络影响力扩散模型—DDIC模型,其采用亲和传播来计算节点之间的激活概率,依据时间片对社会网络进行动态切分,使激活概率在不同时间片中实现了有效关联。实验结果表明DDIC模型中种子节点有更多机会激活它的邻居节点,且采用亲和传播计算出的影响力值能更准确地体现DDIC模型的传播过程。Recently, influence maximization model is a hot issue in the field of social network influence, while the tradi- tional independent cascade model is generally based on static network with a fixed value of activation probability. DDIC model, which was a dynamic network influence diffusion model with attenuation factor was proposed. It calculated the activation probability between nodes via affinity propagation, and according with dynamic segmentation of social net-work time slice, calculation of influence on proliferation of next time slice with the current time slice of activation prob-ability performance decay. The experimental results show that the nodes in the DDIC model have more chances to active the neighbor and the average probability of activing of the DDIC model is higher. Further experiments show that influence value via computing with affinity propagation can reflect the process of the spread model more accurately.

关 键 词：动态社会网络 影响力扩散 亲和传播 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]