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。
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.
[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.
