基于Petri网的微博网络信息传播模型

doi:10.16381/j.cnki.issn1003-207x.2018.12.015

中国管理科学 ›› 2018, Vol. 26 ›› Issue (12): 158-167.doi: 10.16381/j.cnki.issn1003-207x.2018.12.015

基于Petri网的微博网络信息传播模型

刘宇, 梁循, 杨小平

中国人民大学信息学院, 北京 100872

收稿日期:2017-06-29 修回日期:2018-01-17 出版日期:2018-12-20 发布日期:2019-02-25
通讯作者: 梁循(1965-),男(汉族),北京人,中国人民大学信息学院,教授,研究方向:互联网信息分析、数据挖掘、商务智能、社会计算,E-mail:xliang@ruc.edu.cn. E-mail:liang@ruc.edu.cn
基金资助:
国家自然科学基金重点项目（71531012）；国家社会科学基金重大项目（18ZDA309）；北京市自然科学基金资助项目（4172032）

Information Propagation Model of Microblog Network Based on Petri Nets

LIU Yu, LIANG Xun, YANG Xiao-ping

School of Information, Renmin University of China, Beijing 100872, China

Received:2017-06-29 Revised:2018-01-17 Online:2018-12-20 Published:2019-02-25

摘要/Abstract

摘要： 微博网络中的信息传播模型是分析用户行为，找出传播路径，确定领袖人物，发现舆情热点等研究的基础。虽然多种不同角度的信息传播模型已经得到广泛研究，但缺乏对信息动态传播过程的直观描述。本文基于Petri网的结构和特征，针对微博网络信息传播过程，提出了一种简单直观的描述模型，该模型首先对微博网络的信息动态传播过程中的对象进行结构化描述。本文根据微博网络的用户结构关系，并利用Petri网的相关理论，形式化解释和定义信息传播基本对象，从而更加直接描述了微博网络中的转发、评论、回复等多种用户行为。在此基础上，本文利用Petri网能够描述信息流动问题的特征，结合颜色Petri网和时延Petri网，从Petri网系统的角度表示信息动态传播路径，并研究网络的动态性质和传播条件，使得信息传播模型更加真实地模拟信息传播情况。最后本文分析信息传播算例和新浪微博真实数据实验，验证了模型的有效性和可行性，为舆情态势分析以及用户行为的研究提供帮助和支持，同时也为其它社交网络信息传播的用户行为描述提供了新的思路。

关键词: Petri网, 用户在线行为, 社交网络, 动态信息传播

Abstract: Information propagation model in the micro-blog is the basis of many researches, including analyzing user behavior, discovering the ways of spreading, confirming leaders and detecting public opinion hot spots. At present, a1though there has been extensive research on information propagation models which from different angles, these models often lack the intuitionistic description for the information dissemination process. Based on the structure and characteristics of Petri nets, a simple and intuitive description model is presented for the process of information dissemination in micro-blog network. This model first describes the objects in the process of dynamic information dissemination in micro-blog network.According to the micro-blog network information dissemination process, a simple and intuitive description model based on Petri nets is proposed. This paper uses the theory of Petri nets to explain and define the basic objects of message propagation based on the user structure of micro-blog network, and more directly shows the user behavior, including reposting, commenting, replying and so on. On this basis, in the combination with colored Petri nets and timed Petri nets, the feature of Petri nets is used to present the message transmission path from the perspective of Petri nets system. the dynamic property and propagation conditions of micro-blog network are also studied, so that the information propagation model can simulate the message transmission more actually. Finally, the case study of the information diffusion model is carriedon and real data on Sina micro-blog are used to do experiments and analysis, which demonstrate the feasibility and availability of the model and provide supports for public opinion analysis and user behavior research. Meanwhile, this model can further provide a new idea for describing user behavior in message propagation system of other social networks.

Key words: Petri nets, online user behavior, social network, dynamic information propagation

中图分类号:

TP391
X913.4

刘宇, 梁循, 杨小平. 基于Petri网的微博网络信息传播模型[J]. 中国管理科学, 2018, 26(12): 158-167.

LIU Yu, LIANG Xun, YANG Xiao-ping. Information Propagation Model of Microblog Network Based on Petri Nets[J]. Chinese Journal of Management Science, 2018, 26(12): 158-167.

参考文献

[1] Ellison N B. Social network sites:Definition, history, and scholarship[J]. Journal of Computer-Mediated Communication, 2007, 13(1):210-2303.

[2] Kempe D, Kleinberg J, Tardos É. Maximizing the spread of influence through a social network[C]//Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, D.C, August 24-27,2013:137-146.

[3] Watts D J. A simple model of global cascades on random networks[J]. Proceedings of the National Academy of Sciences, 2002, 99(9):5766-5771.

[4] 李栋, 徐志明, 李生. 在线社会网络中信息扩散[J]. 计算机学报, 2014, 37(1):189-206.

[5] Granovetter M. Threshold models of collective behavior[J]. American Journal of Sociology, 1978,83(6):1420-1443.

[6] 曹玖新, 董丹, 徐顺. 一种基于k-核的社会网络影响最大化算法[J]. 计算机学报, 2015, 38(2):238-248.

[7] Fan Wei, Yeung K H. Virus propagation modeling in Facebook[C]//Proceedings of 2010 International Conference on Advances in Social Networks Analysis and Mining, Odense, Denmark Aug 9-11, 2010.

[8] Xiong Fei, Liu Yun, Zhang Zhenjiang, et al. An information diffusion model based on retweeting mechanism for online social media[J]. Physics Letters A, 2012, 376(30-31):2103-2108.

[9] Muroya Y, Enatsu Y, Li Huaixing. Global stability of a delayed SIRS computer virus propagation model[J]. International Journal of Computer Mathematics, 2014, 91(3):347-367.

[10] Prakash B A, Tong Hanghang, Valler N,et al. Virus propagation on time-varying networks:Theory and immunization algorithms[C]//Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Barcelona, Spain, Sepeember 19-23, 2010:99-114.

[11] 李勇建, 王循庆, 乔晓娇. 基于广义随机Petri网的重大传染病传播演化模型研究[J]. 中国管理科学, 2014, 22(3):74-81.

[12] Jiang Chunxiao, Chen Yan, Liu K J R. Evolutionary dynamics of information diffusion over social networks[J]. IEEE Transactions on Signal Processing, 2014, 62(17):4573-4586.

[13] 郭东伟, 乌云娜, 邹蕴. 基于非理性博弈的舆情传播仿真建模研究[J]. 自动化学报, 2014, 40(8):1721-1732.

[14] 王光辉, 刘怡君. 网络舆论危机事件的蔓延扩散效应研究[J]. 中国管理科学, 2015, 23(7):119-126.

[15] Bian Jingwen, Yang Yang, Chua T S. Predicting trending messages and diffusion participants in microblogging network[C]//Proceedings of the 37th International ACM SIGIR Conference on Research &Development in Information Retrieval, Gold Coast, Queensland, Australia, July 06-11, 2014:537-546.

[16] Iribarren J L, Moro E. Impact of human activity patterns on the dynamics of informationdiffusion[J]. Physical Review Letters, 2009, 103(3):038702.

[17] Cha M, Haddadi H, Benevenuto F, et al. Measuring user influence in Twitter:The million follower fallacy[J]. International AAAI Conference on Weblogs and Social Media, 2010, 10(10-17):30.

[18] Hong Liangjie, Dan O, Davison B D. Predicting popular messages in Twitter[C]//Proceedings of the 20th International Conference Companion on World Wide Web, Hyderabad, India, March 28-April 01, 2011:57-58.

[19] Gyarmati L, Trinh T A. Measuring user behavior in online social networks[J]. IEEE Network, 2010, 24(5):26-31.

[20] 景楠, 王建霞, 许皓. 基于用户社会关系的社交网络好友推荐算法研究[J]. 中国管理科学, 2017, 25(3):137-146.

[21] 程晓涛, 刘彩霞, 刘树新. 基于关系图特征的微博水军发现方法[J]. 自动化学报, 2015,41(9):1533-1541.

[22] Murata T. Petri nets:Properties, analysis and applications[J]. Proceedings of the IEEE, 1989, 77(4):541-580.

[23] 吴哲辉. Petri网导论[M]. 北京:机械工业出版社, 2006.

[24] 袁祟义. Petri网原理与应用[M]. 北京:电子工业出版社, 2005.

[25] Jensen K. Coloured Petri nets:Basic concepts, analysis methods and practical use[M]. Berlin:Springer Science & Business Media, 2013.

[26] Zuberek W M. Timed Petri nets definitions, properties, and applications[J]. Microelectronics Reliability, 1991, 31(4):627-644.

[27] Ren Donghao, Zhang Xin, Wang Zhenhuang. WeiboEvents:A crowd sourcing weibovisual analytic system[C]//Proceedings of 2014 IEEE Pacific Visualization Symposium, Yokohama, Japan, March 4-7, 2014:330-334.

基于Petri网的微博网络信息传播模型

Information Propagation Model of Microblog Network Based on Petri Nets

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	刘世峰,康来松,宫大庆. 面向群组的事件兴趣点推荐算法研究[J]. 中国管理科学, 2023, 31(12): 301-310.
[2]	田世海,王春梦,杨文蕊. 基于ANP和随机Petri网的突发事件网络舆情危机预警机制研究[J]. 中国管理科学, 2023, 31(10): 215-224.
[3]	焦媛媛, 闫鑫, 杜军, Roger Jiao. 社交网络情境下基于同侪影响的产品线规划研究[J]. 中国管理科学, 2022, 30(12): 108-119.
[4]	倪培昆, 朱建明, 王国庆. 在线社交网络虚假信息交互量最小化的边阻断策略研究[J]. 中国管理科学, 2021, 29(9): 188-200.
[5]	顾秋阳, 吴宝, 池仁勇. 考虑思考时滞的社交网络舆情最优控制模型[J]. 中国管理科学, 2021, 29(11): 215-223.
[6]	王治莹, 李勇建, 王伟康. 基于随机Petri网的突发事件信息演化模型[J]. 中国管理科学, 2020, 28(3): 113-121.
[7]	屈绍建, 卢艳玲, 纪颖. 社交网络推送下的众筹融资机制分析[J]. 中国管理科学, 2019, 27(3): 1-10.
[8]	孙华丽, 赵喆, 刘涛, 薛耀锋. 震后应急医疗救援流程效率评价研究[J]. 中国管理科学, 2019, 27(1): 205-216.
[9]	米传民, 李丹丹, 张婷, 钱媛媛, 周志鹏. 考虑社交网络和互联网金融的金融市场超网络均衡研究[J]. 中国管理科学, 2018, 26(12): 56-65.
[10]	杨立, 赵翠翠, 陈晓红. 基于社交网络的P2P借贷信用风险缓释机制研究[J]. 中国管理科学, 2018, 26(1): 47-56.
[11]	车诚, 戚晓琳, 马万祺, 邵冬雁. 移动社交网络营销效果的影响因素实证研究[J]. 中国管理科学, 2017, 25(5): 145-149.
[12]	景楠, 王建霞, 许皓, 卞亦文. 基于用户社会关系的社交网络好友推荐算法研究[J]. 中国管理科学, 2017, 25(3): 164-171.
[13]	李勇建, 王循庆, 乔晓娇. 基于广义随机Petri网的重大传染病传播演化模型研究[J]. 中国管理科学, 2014, 22(3): 74-81.
[14]	韩文民, 袁丽丽, 叶涛锋. 基于随机Petri网的生产提前期牛鞭效应测度研究[J]. 中国管理科学, 2011, 19(2): 116-124.
[15]	刘远, 方志耕, 刘思峰, 郭本海. 灰信息下关键设备生产缓冲Petri网模型研究[J]. 中国管理科学, 2010, 18(4): 107-113.