智能多Agent网络的微信信息传播仿真研究

doi:10.11925/infotech.1003-3513.2015.06.13

摘要
参考文献
相关文章
Metrics

全文: PDF(965 KB) HTML
输出: BibTeX | EndNote (RIS)

摘要

【目的】在分析微信区别于社交媒体平台新特点的基础上, 通过仿真研究微信信息传播机理。【方法】分析微信信息的交互规律并构建复杂网络, 在对相关影响变量进行探究的基础之上建立多Agent模型, 并提出三种基于各变量的Agent间演化规则。【结果】仿真实验结果表明, 该模型模拟结果与微信信息传播的宏观特征相吻合, 提出的主要影响变量对更好地管控和利用微信信息传播具有重要的启示意义。【局限】影响微信信息传播的相关变量未能全面涉及, 并且因缺乏微信用户数据, 所构建的微信信息传播网络与真实情况有差异。【结论】有利于揭示微信信息传播的关键机理, 并有助于对微信平台进行有效利用和管控。

	服务

	把本文推荐给朋友
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	王小立

关键词 ：复杂网络建模, 多Agent建模, 信息传播, 演化规则

Abstract：

[Objective] Based on the analysis of the new characteristics of WeChat which are different from social media platforms, this paper researches the mechanism of WeChat information diffusion with the simulation method. [Methods] The complex network is established through the analysis on the interactive rule of information, the agent model is built upon the work searching for relevant variables, and three evolution rules based on these variables are presented for the information interaction between agents. [Results] The simulation experiments demonstrate that the simulation results coincide with the information diffusion macroscopic features of WeChat and the proposed primary variables have enlightenment to both controlling and using WeChat information diffusion. [Limitations] All the variables which affect the information diffusion can not be introduced, and there is discrepancy between the complex network and the real world social network due to the lack of the data. [Conclusions] This study reveals the mechanism of WeChat information diffusion and contributes to controlling and making better use of WeChat.

Key words： Complex-network-based modeling Multi-agent-based modeling Information diffusion Evolution rule

收稿日期: 2014-11-14

TP393

通讯作者: 王小立, ORCID: 0000-0002-5010-580X, E-mail: 2002wangtian.student@sina.com。 E-mail: 2002wangtian.student@sina.com

引用本文:

王小立. 智能多Agent网络的微信信息传播仿真研究[J]. 现代图书情报技术, 2015, 31(6): 85-92.
Wang Xiaoli. Simulation Research on WeChat Information Diffusion Based on Intelligent Multi-agent Networks. New Technology of Library and Information Service, DOI：10.11925/infotech.1003-3513.2015.06.13.

链接本文:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.1003-3513.2015.06.13

[1] 中国互联网信息中心. 第34次中国互联网络发展状况统计报告[R/OL]. [2014-11-25]. http://www.cnnic.net.cn/hlwfzyj/ hlwxzbg/. (CNNIC. The 34th China Internet Network Development State Statistic Report [R/OL]. [2014-11-25]. http://www.cnnic.net.cn/hlwfzyj/hlwxzbg/.
[2] 方兴东, 石现升, 张笑容, 等. 微信传播机制与治理问题研究[J]. 现代传播, 2013(6): 122-127. (Fang Xingdong, Shi Xiansheng, Zhang Xiaorong, et al. Research on Mechanism of WeChat Information Diffusion and Management [J]. Modern Communication, 2013(6): 122-127.)
[3] 柳军, 蔡淑琴. 微内容的网络舆情传播特征分析[J]. 情报杂志, 2013, 32(1): 1-4. (Liu Jun,Cai Shuqin. Research on Characteristics of Network Public Opinion Communication of the Micro-content [J]. Journal of Intelligence, 2013, 32(1): 1-4.)
[4] 郑怡文, 白云晖. 微信的传播生态审视[J].前沿, 2013(16): 165-166. (Zheng Yiwen, Bai Yunhui. Survey to Communication Ecology of WeChat [J]. Forward Position, 2013(16): 165-166.)
[5] 赵振祥, 王洁.微博与微信: 基于媒介融合的比较研究[J].编辑之友, 2013(12): 50-52. (Zhao Zhenxiang, Wang Jie. Comparative Study of Microblog and WeChat Based on Media Convergence [J]. Editorial Friend, 2013(12): 50-52.)
[6] 李青, 朱恒民, 杨东超. 微博网络中舆情话题传播演化模型[J]. 现代图书情报技术, 2013(12): 74-80. (Li Qing, Zhu Hengmin, Yang Dongchao. The Topic Evolution Model of the Public Opinion in Micro-blogging Network [J]. New Technology of Library and Information Service, 2013(12): 74-80.)
[7] 魏静, 朱恒民, 洪小娟,等.基于复杂在线网络的舆情传递研究——进化博弈视角[J]. 现代图书情报技术, 2013(3): 65-70. (Wei Jing, Zhu Hengmin, Hong Xiaojuan, et al. Study of Public Sentiment Transfer Based on Complex Online Network——Evolutionary Game Angle [J]. New Technology of Library and Information Service, 2013(3): 65-70.)
[8] Hegselmann R, Krause U. Opinion Dynamics and Bounded Confidence: Models, Analysis and Simulation [J]. Journal of Artificial Societies and Social Simulation, 2002, 5(3): 2-34.
[9] Slanina F, Lavicka H. Analytical Results for the Sznajd Model of Opinion Formation [J]. The European Physical Journal B, 2003, 35(2): 279-288.
[10] Schulze C. Sznajd Opinion Dynamics with Global and Local Neighbourhood [J]. International Journal of Modern Physics C, 2004, 15(6): 867-872.
[11] Rodrigues F A, da F Costa Luciano. Surviving Opinions in Sznajd Models on Complex Network [J]. International Journal of Modern Physics C, 2005, 16(11): 1785-1792.
[12] Toscani G. Kinetic Models of Opinion Formation [J]. Communications in Mathematical Sciences, 2006, 4(3): 481-496.
[13] Zhou J, Liu Z, Li B. Influence of Network Structure on Rumor Propagation [J]. Physics Letters A, 2007, 368(6): 458-463.
[14] 曾祥平, 方勇, 袁媛, 等. 基于元胞自动机的网络舆论激励模型[J]. 计算机应用, 2007, 27(11): 2686-2688. (Zeng Xiangping, Fang Yong, Yuan Yuan, et al. Motivation Model for Online Public Opinion Based on Cellular Automata [J]. Journal of Computer Applications, 2007, 27(11): 2686-2688.)
[15] 兰月新, 邓新元. 突发事件网络舆情演进规律模型研究[J]. 情报杂志, 2011, 30(8): 47-50. (Lan Yuexin, Deng Xinyuan. Research on the Evolution Model of Network Public Opinion of Sudden Events [J]. Journal of Intelligence, 2011, 30(8): 47-50.)
[16] 方薇, 何留进, 宋良图. 因特网舆情传播的协同元胞自动机模型[J]. 计算机应用, 2012, 32(2): 399-402. (Fang Wei, He Liujin, Song Liangtu. Synergistic Cellular Automata Model for Dissemination of Internet Public Opinion [J]. Journal of Computer Applications, 2012, 32(2): 399-402.)
[17] 王根生. 无标度特性下的网络舆情演化迁移元胞模型[J]. 小型微型计算机系统, 2013, 34(5): 1085-1090. (Wang Gensheng. Tow-stages Model for the Evolution of Network Public Opinion on Scale-free Characteristics [J]. Journal of Chinese Computer Systems, 2013, 34(5): 1085-1090.)
[18] Newman M E J. The Structure and Function of Complex Networks [J]. SIAM Review, 2003, 45(2): 167-256.
[19] Agiza H N, Elgazzar A S, Youssef S A. Phase Transitions in Some Epidemic Models Defined on Small-world Networks [J]. International Journal of Modern Physics C, 2003, 14(6): 825-833.
[20] Moreno Y, Pastor-Satorras R, Vespignani A. Epidemic Outbreaks in Complex Heterogeneous Networks [J]. The European Physical Journal B, 2002, 26(4): 521-529.
[21] Barabasi A L, Albert R. Emergence of Scaling in Random Networks [J]. Science, 1999, 286(5439): 509-512.
[22] 伊丽莎白·诺尔·诺依曼. 沉默的螺旋: 舆论——我们的社会皮肤[M]. 董璐译. 北京: 北京大学出版社, 2013. (Elisabeth Noelle Neumann. The Spiral of Silence: Public Opinion—Our Social Skin [M]. Translated by Dong Lu. Beijing: Peking University Press, 2013.)

[1]	王晰巍,张柳,李师萌,王楠阿雪. *新媒体环境下社会公益网络舆情传播研究^ ——以新浪微博“画出生命线”话题为例**[J]. 数据分析与知识发现, 2017, 1(6): 93-101.
[2]	廖海涵, 王曰芬. *社交媒体舆情信息传播效果影响因素研究^——以新浪微博“8.12天津爆炸”事件为例**[J]. 数据分析与知识发现, 2016, 32(12): 85-93.
[3]	何建民, 王哲. 社交网络话题信息传播影响簇发现谱系挖掘方法[J]. 现代图书情报技术, 2015, 31(5): 65-72.
[4]	马海群. 论主页设计的知识产权法律保护方式与保护内容[J]. 现代图书情报技术, 1999, 15(6): 49-51.

Viewed

Full text

Abstract

Cited

Shared

Discussed