《计算机应用研究》|Application Research of Computers

基于邻居节点间相互影响和改进概率的社交网络信息传播模型

Information propagation model with improved probability based on influence of neighbors for social network

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作者 张永,和凯
机构 兰州理工大学 计算机与通信学院,兰州 730050
统计 摘要被查看 次,已被下载
文章编号 1001-3695(2018)03-0755-05
DOI 10.3969/j.issn.1001-3695.2018.03.024
摘要 目前网络传播动力学的研究焦点之一是以经典的传染病动力学模型为基础,研究特定网络的信息传播规律。针对社交网络中信息传播的特点,在传统的SIR模型基础上,通过加入新的一类假免疫节点,建立了新的SDIR模型。考虑到邻居节点间的相互影响,通过定义三个传播概率函数,对SDIR模型作了改进,得到了更加符合社交网络特点的传播模型。对比不同条件下信息传播的过程,实验证明了信息不能覆盖全网络,Twitter比新浪微博有更好的信息传播效率的推测,并发现初始传播概率会对信息传播有重要影响。
关键词 社交网络;信息传播;SDIR模型
基金项目
本文URL http://www.arocmag.com/article/01-2018-03-024.html
英文标题 Information propagation model with improved probability based on influence of neighbors for social network
作者英文名 Zhang Yong, He Kai
机构英文名 CollegeofComputer&Communication,LanzhouUniversityofTechnology,Lanzhou730050,China
英文摘要 One of current focus of spreading dynamics research analses the propagation of information in the specific network based on the epidemic dynamics model. According to the characteristics of propagation in social network, this paper added a kind of new node named disguising node based on the susceptible-infectious-recovered(SIR) model, and proposed a model named susceptible-disguising-infectious-recovered (SDIR) to describe the propagation better in social network. Considering the mutual influence of neighbor nodes, it defined three propagation probability functions to improve the SDIR model. The results show, by simulating propagation under different conditions, that information cannot cover the whole network, and Twitter performs better than Sina Micro-blog in efficiency of propagating. Also, the initial infection probability has a significant influence in the information propagation.
英文关键词 social network; information propagation; SDIR model
参考文献 查看稿件参考文献
  [1] 中国互联网络信息中心. 第37次中国互联网络发展状况统计报告[EB/OL] . (2016-01-22)[2016-10-30] . http://www. cnnic. cn/gywm/xwzx/rdxw/2016/201601/W020160122639198410766. pdf.
[2] Zhao Laijun, Wang Jiajia, Chen Yucheng, et al. SIHR rumor spreading model in social networks[J] . Physica A, 2012, 391(7):2444-2453.
[3] 顾亦然, 夏玲玲. 在线社交网络中谣言的传播与抑制[J] . 物理学报, 2012, 61(23):238701.
[4] 王辉, 韩江洪, 邓林, 等. 基于移动社交网络的谣言传播动力学研究[J] . 物理学报, 2013, 62(11):110505.
[5] 曹玖新, 吴江林, 石伟, 等. 新浪微博网信息传播分析与预测[J] . 计算机学报, 2014, 37(4):779-782.
[6] Hong Liangjie, Dan O, Davison B D. Predicting popular messages in Twitter[C] //Proc of the 20th International Conference on Companion on World Wide Web. 2011:57.
[7] Dickens L, Molloy I, Lobo J. Learning stochastic models of information flow[C] //Proc of the 28th IEEE International Conference on Data Engineering. 2012:570.
[8] 张彦超, 刘云, 张海峰, 等. 基于在线社交网络的信息传播模型[J] . 物理学报, 2011, 60(5):60-66.
[9] Lyu Linyuan, Chen Duanbing, Zhou Tao. Small world yields the most effective information spreading[J] . New Journal of Physics, 2011, 13(12):825-834.
[10] 王金龙, 刘方爱, 朱振方. 一种基于用户相对权重的在线社交网络信息传播模型[J] . 物理学报, 2015, 64(5):63-73.
[11] 唐朝生. 在线社交网络信息传播建模及转发预测研究[D] . 秦皇岛:燕山大学, 2014.
[12] Newman M E. A measure of betweenness centrality based on random walks[J] . Social Networks, 2005, 27(1):39-54.
[13] Moreno Y, Nekovee M, Pacheco A F. Dynamics of rumor spreading in complex networks[J] . Physical Review E, 2004, 69(2):279-307.
[14] Chen Duanbing, Lyu Linyuan, Shang Mingsheng, et al. Identifying influential nodes in complex networks[J] . Physica A, 2012, 391(4):1777-1787.
[15] Kitsak M, Gallos L K, Havlin S, et al. Identification of influential spreaders in complex networks[J] . Nature Physics, 2010, 6(11):888-893.
[16] Liu Jianguo, Ren Zhuoming, Guo Qiang. Ranking the spreading influence in complex networks[J] . Physica A:Statistical Mechanics and Its Applications, 2013, 392(18):4154-4159.
[17] Hou Bonan, Yao Yiping, Liao Dongsheng. Identifying all-around nodes for spreading dynamics in complex networks[J] . Physica A:Statistical Mechanics and Its Applications, 2012, 391(15):4012-4017.
[18] Hu Qingcheng, Gao Yang, Ma Pengfei, et al. A new approach to identify influential spreaders in complex networks[M] //Information Management. Berlin:Springer, 2013:99-104.
[19] Sznajd-Weron K. Sznajd model and its applications[J] . Acta Physica Polonica B, 2005, 36(8):2537-2547.
[20] Zheng Muhua, Lyu Linyuan, Zhao Ming. Spreading in online social networks:the role of social reinforcement[J] . Physical Review E, 2013, 88(1):012818.
[21] Centola D. The spread of behavior in an online social network experiment[J] . Science, 2010, 329(5996):1194-1197.
收稿日期 2016/11/16
修回日期 2017/1/10
页码 755-759,764
中图分类号 TP393
文献标志码 A