为更加真实地反映病毒营销中信息的竞争传播,基于经典SIS病毒传播模型建立了一个具有部分交叉免疫的信息竞争传播模型,描述两种传播概率不同的既有合作又有竞争关系信息的传播过程。仿真结果表明:当两信息完全排斥时,支持“赢者通吃”的经典生态学结论;当两种信息间存在一定合作时,合作概率越大较弱病毒存活的规模越大,但弱病毒需要依靠与强病毒的合作而存在;网络度分布越异质越有利于合作。
In order to more truly reflect the competition and cooperation behavior in viral marketing, based on the classic propagation model SIS, a propagation model with partial cross-immunity is proposed. The model describes the propagation of two viruses which enjoy both cooperative and competitive relationship and have different propagation probability. Further, we in depth study two kinds of viral marketing information propagation characteristics with different network structure. Simulation results show that there is a phase transition: If the competition is harsh, then we can get the same results as classic ecology studies i.e. ‘winner takes all’; otherwise, the weaker information will survive. And the higher the probability of cooperation is the greater scale the weaker virus survives in. Simulation results also show that the weaker virus can survive only when it cooperates with the stronger one. Heterogeneous distribution of degree is conducive to cooperation.
[1] Easley D, Kleinberg J. Networks, crowds, and markets[J]. Cambridge Univ Press, 2010, 6(1): 6.1.
[2] 胡海波, 王科, 徐玲,等. 基于复杂网络理论的在线社会网络分析[J]. 复杂系统与复杂性科学, 2008, 5(2): 1-14.
Hu Haibo,Wang Ke,Xu Ling, et al. Analysis of online social networks based on complex network theory[J]. Complex System and Complexity Science, 2008, 5(2): 1-14.
[3] 徐雪娟, 郭进利, 何静. 网络购物行为的人类动力学模式[J]. 复杂系统与复杂性科学, 2013, 10(4): 69-75.
Xu Xuejuan,Guo Jinli,He Jing. Human activity pattern on on-line shopping[J]. Complex System and Complexity Science, 2013, 10(4): 69-75.
[4] Kostka J, Oswald Y A, Wattenhofer R. Word of Mouth: Rumor Dissemination in Social Networks in Structural Information and Communication Complexity[M].Berlin Heidelberg: Springer, 2008:185-196.
[5] Leskovec J, Adamic L A, Huberman B A. The dynamics of viral marketing[J]. ACM Transactions on the Web (TWEB), 2007, 1(1): 1-46.
[6] Prakash B A, Beutel A, Rosenfeld R, et al. Winner takes all: competing viruses or ideas on fair-play networks[C]//Proceedings of the 21st international conference on World Wide Web. Lyon: ACM, 2012: 1037-1046.
[7] Frank R H, Cook P J. The Winner-Take-All Society: Why the Few at the Top Get so Much More Than the Rest of Us[M]. London: Virgin Books, 2010:28-59.
[8] Wang Y, Xiao G, Liu J. Dynamics of competing ideas in complex social systems[J]. New Journal of Physics, 2012, 14(1): 013015.
[9] Karrer B, Newman M. Competing epidemics on complex networks[J]. Physical Review E, 2011, 84(3): 036106.
[10] Wu Q, Small M, Liu H. Superinfection behaviors on scale-free networks with competing strains[J]. Journal of Nonlinear Science, 2013, 23(1): 113-127.
[11] Lipsitch M, Colijn C, Cohen T, et al. No coexistence for free: neutral null models for multistrain pathogens[J]. Epidemics, 2009, 1(1): 2-13.
[12] Kempe D, Kleinberg J, Tardos é. Maximizing the spread of influence through a Social Network[C]//Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2003: 137-146.
[13] Shakarian P, Paulo D. Large social networks can be targeted for viral marketing with small seed sets[C]//Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012). Istanbul Turkey: IEEE Computer Society, 2012: 1-8.
[14] Dinh TN, Nguyen DT, Thai MT. Cheap, easy, and massively effective viral marketing in social networks: truth or fiction?[C]//Proceedings of the 23rd ACM conference on Hypertext and Social Media. Milwaukee: ACM, 2012: 165-174.
[15] Anderson R M, May R M, Anderson B. Infectious Diseases of Humans: Dynamics and Control[M]. New Jersey: Wiley Online Library, 1992:20-58.
[16] Watts D J, Strogatz S H. Collective dynamics of ‘small-world’ networks[J].Nature, 1998, 393(6684): 440-442.
[17] Barabási A-L, Albert R. Emergence of scaling in random networks[J].Science, 1999, 286(5439): 509-512.
[18] Pastor-Satorras R, Vespignani A. Epidemic dynamics and endemic states in complex networks[J]. Physical Review E, 2001, 63(6): 066117.
[19] Pastor-Satorras R, Vespignani A. Epidemic spreading in scale-free networks[J].Physical Review Letters, 2001, 86(14): 3200.
[20] Riley S. Large-scale spatial-transmission models of infectious disease[J].Science, 2007, 316(5829): 1298-1301.
[21] Grassly N C, Fraser C. Mathematical models of infectious disease transmission[J].Nature Reviews Microbiology, 2008, 6(6): 477-487.
[22] Ahn Y Y, Jeong H, Masuda N, et al. Epidemic dynamics of two species of interacting particles on scale-free networks[J]. Physical Review E, 2006, 74(6): 066113.
[23] Beutel A, Prakash B A, Rosenfeld R, et al. Interacting viruses in networks: can both survive?[C]//Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Beijing: ACM, 2012: 426-434.
[24] 邵峰晶,孙仁诚,李淑静. 一种具有抑制作用的多信息传播模型[J].复杂系统与复杂性科学, 2010, 7(1):47-51.
Shao Jingfeng,Sun Rencheng,Li Shujing. A model of multi-Information dissemination with suppressed action[J]. Complex System and Complexity Science, 2010, 7(1):47-51.
[25] Sun Y, Liu C, Zhang C, et al. Epidemic spreading on weighted complex networks[J]. Physics Letters A, 2014, 378(7): 635-640.