复杂网络下软件扩散多智能体仿真研究
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摘要
随着互联网技术的飞速发展,越来越多的软件借助互联网进行营销和扩散。本研究结合复杂网络理论和多智能体仿真技术,研究了网络效应软件和恶意软件的扩散动力学特征和微观作用机制,建立网络效应软件扩散仿真模型和恶意软件扩散仿真模型,对于指导软件企业制定营销策略和政府部门控制恶意软件扩散有重要的现实意义。
与传统的产品相比,网络效应软件的扩散网络交织着人际网络和互联网两种网络。本文描述了网络效应软件的扩散动力学,建立了网络效应软件的价值函数和扩散网络中消费者的采用决策函数,构建了基于双网络的网络效应软件扩散多智能体仿真模型,分析了扩散网络拓扑结构、消费者内部异质性、消费者外部异质性及网络效应强度对网络效应软件扩散的影响,并得出相关结论。研究结果表明,扩散网络拓扑结构对网络效应软件扩散存在显著的影响,软件产品在双网络中的扩散速度最快,也最先到达扩散稳态;消费者内部异质性越强,网络效应软件扩散速度越快;仅当扩散网络类型为异质型时,消费者外部异质性对网络效应软件扩散存在影响,软件产品的扩散速度比消费者外部同质时快;网络效应强度越强,网络效应软件后期的扩散速度越快。
恶意软件的扩散不同于传染病扩散在于它是利益驱动的。基于传染病扩散模型,本文分析恶意软件扩散主机、持久免疫主机和普通主机的微观作用机制,建立基于无标度网络的恶意软件扩散多智能体仿真模型,探讨持久免疫主机的比例、普通主机学习效率和普通主机抵抗力异质性对恶意软件扩散的影响。研究表明增加持久免疫主机比例能有效限制恶意软件的扩散;普通主机学习效率的增强直接减少被感染主机的数量,并决定恶意软件扩散的成败;普通主机抵抗力异质性对恶意软件扩散没有影响,只有提高网民解决恶意软件的能力才能遏制恶意软件的扩散。
With the rapid development of Internet technologies, more and more software have used the Internet for marketing and diffusing. This study combines complex networks theory and multi-agent simulation technology to explore the diffusion dynamics and micro-mechanism of network effect software and malicious software, establish simulation models of the network effect software and the malicious software diffusion, which can guide the software enterprises to develop marketing strategy and government departments to control the spread of malicious software. This has very important practical significance.
Compared to traditional products, the diffusion networks of network effect software intertwine interpersonal networks and the Internet. This study describes diffusion dynamics of the network effect software, establishes the utility function of network effect software and decision-making function of consumers, builds multi-agent simulation model of the network effect software diffusion which based on dual-networks, analyzes impacts of the diffusion network topology structure, the internal and external heterogeneity of consumers, the intensity of network effect on the software diffusion. The study draws some beneficial conclusions. The diffusion networks topology structure has significant impact on the diffusion of network effect software. The speed of software products diffusion in the dual-network is fastest, and the curve of diffusion reaches the steady-state firstly. The more the internal heterogeneity of consumers is, the faster the speed of software products diffusion is. Only if the diffusion networks are heterogeneous, the external heterogeneity of consumers can accelerate the speed of software products diffusion. The more the intensity of network effects is, the faster speed of software products diffusion is at the later stage.
The diffusion of malicious software is different from the diffusion of infectious diseases. Because it was driven by interest. Based on the infectious diseases spread model, this study analyzes the micro-mechanism of malicious software spread host, lasting immune host and ordinary host, builds multi-agent simulation model of the malicious software diffusion which based on scale-free networks, and explores the influence of the proportion of lasting immune host, the learning efficiency of the ordinary host, the heterogeneity of ordinary host’s resistance on the spread of malicious software. The research results show that increasing proportion of the lasting immune host can effectively limit the spread of malicious software. Enhancing learning efficiency of the ordinary host directly reduce the number of infected hosts. The learning efficiency determines the success or failure of the spread of malicious software. The heterogeneity of ordinary host’s resistance has no impact on the malicious software diffusion. Only enhancing the ability of netizens to solve the malicious software can curb the spread of malicious software.
与传统的产品相比,网络效应软件的扩散网络交织着人际网络和互联网两种网络。本文描述了网络效应软件的扩散动力学,建立了网络效应软件的价值函数和扩散网络中消费者的采用决策函数,构建了基于双网络的网络效应软件扩散多智能体仿真模型,分析了扩散网络拓扑结构、消费者内部异质性、消费者外部异质性及网络效应强度对网络效应软件扩散的影响,并得出相关结论。研究结果表明,扩散网络拓扑结构对网络效应软件扩散存在显著的影响,软件产品在双网络中的扩散速度最快,也最先到达扩散稳态;消费者内部异质性越强,网络效应软件扩散速度越快;仅当扩散网络类型为异质型时,消费者外部异质性对网络效应软件扩散存在影响,软件产品的扩散速度比消费者外部同质时快;网络效应强度越强,网络效应软件后期的扩散速度越快。
恶意软件的扩散不同于传染病扩散在于它是利益驱动的。基于传染病扩散模型,本文分析恶意软件扩散主机、持久免疫主机和普通主机的微观作用机制,建立基于无标度网络的恶意软件扩散多智能体仿真模型,探讨持久免疫主机的比例、普通主机学习效率和普通主机抵抗力异质性对恶意软件扩散的影响。研究表明增加持久免疫主机比例能有效限制恶意软件的扩散;普通主机学习效率的增强直接减少被感染主机的数量,并决定恶意软件扩散的成败;普通主机抵抗力异质性对恶意软件扩散没有影响,只有提高网民解决恶意软件的能力才能遏制恶意软件的扩散。
With the rapid development of Internet technologies, more and more software have used the Internet for marketing and diffusing. This study combines complex networks theory and multi-agent simulation technology to explore the diffusion dynamics and micro-mechanism of network effect software and malicious software, establish simulation models of the network effect software and the malicious software diffusion, which can guide the software enterprises to develop marketing strategy and government departments to control the spread of malicious software. This has very important practical significance.
Compared to traditional products, the diffusion networks of network effect software intertwine interpersonal networks and the Internet. This study describes diffusion dynamics of the network effect software, establishes the utility function of network effect software and decision-making function of consumers, builds multi-agent simulation model of the network effect software diffusion which based on dual-networks, analyzes impacts of the diffusion network topology structure, the internal and external heterogeneity of consumers, the intensity of network effect on the software diffusion. The study draws some beneficial conclusions. The diffusion networks topology structure has significant impact on the diffusion of network effect software. The speed of software products diffusion in the dual-network is fastest, and the curve of diffusion reaches the steady-state firstly. The more the internal heterogeneity of consumers is, the faster the speed of software products diffusion is. Only if the diffusion networks are heterogeneous, the external heterogeneity of consumers can accelerate the speed of software products diffusion. The more the intensity of network effects is, the faster speed of software products diffusion is at the later stage.
The diffusion of malicious software is different from the diffusion of infectious diseases. Because it was driven by interest. Based on the infectious diseases spread model, this study analyzes the micro-mechanism of malicious software spread host, lasting immune host and ordinary host, builds multi-agent simulation model of the malicious software diffusion which based on scale-free networks, and explores the influence of the proportion of lasting immune host, the learning efficiency of the ordinary host, the heterogeneity of ordinary host’s resistance on the spread of malicious software. The research results show that increasing proportion of the lasting immune host can effectively limit the spread of malicious software. Enhancing learning efficiency of the ordinary host directly reduce the number of infected hosts. The learning efficiency determines the success or failure of the spread of malicious software. The heterogeneity of ordinary host’s resistance has no impact on the malicious software diffusion. Only enhancing the ability of netizens to solve the malicious software can curb the spread of malicious software.
引文
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[3]胡知能.创新产品市场扩散模型及其应用[D].四川:四川大学, 2005.
[4]盛亚,吴健中.新产品市场扩散Bass模型族的研究[J].预测, 1999(2): 71-74.
[5]王朋,孙骅.部分完全替代创新产品的扩散模型[J].系统工程,2005(9): 33-36.
[6]刘超,罗洪林,王君祥.技术创新扩散模型的研究进程与方向[J].工业技术经济,2006,12: 56-60.
[7] Sabine Schmidt, Daniel Baier. System Dynamics Based Prediction of New Product Diffusion: An Evaluation. Springer Berlin Heidelberg, 2006, URL:http://www. springerlink.com/ index/khwp45m52573481q.pdf.
[8] R. Albert, A L. Barabási. Statistical Mechanics of Complex Networks[J]. Rev Mod Phys, 2002(74): 47-97.
[9] D J. Watts, S H. Strogatz. Collective Dynamics of Small world Networks[J]. Nature, 1998(393): 440-442.
[10] Li X, W Xiao fan. Controlling the Spreading in Small-World Evolving Networks: Stability, Oscillation, and Topology[J], IEEE Transactions on Automatic Control, 2006, 51(3): 534-540.
[11] C Zhen Yi, W Xiao Fan. A Congestion Awareness Routing Strategy for Scale-Free Networks with Tunable Clustering[J]. Physica A, 2006(364): 595–602.
[12] M E J. Newman. The Structure and Function of Complex Networks[J]. SIAM Review, 2003(45): 167-256.
[13]汪小帆,李翔,陈关荣.复杂网络理论及其应用[M].北京:清华大学出版社, 2006.
[14]周涛,傅忠谦,牛永伟,王达,曾燕,汪秉宏,周佩玲.复杂网络上传播动力学研究综述[J].自然科学进展, 2005, 5(15):513-518.
[15] D L. Loudon. Consumer Behavior: Concepts and Applications[C]. 4th Ed, McGrew-Hill, 1993.
[16] U O. Sabine, Hara, Sigrid Stagl. Endogenous Preferences and Sustainable Development. Journal of Socio-Economics[C], 2002(4): 511-527.
[17] D B. Holt. Poststructuralist Lifestyle Analysis: Conceptualizing the Social Post modernity[J]. Journal of Consumer Research, 1997(4): 326-350.
[18]陈荣,余亮,何宜柱.元胞自动机模拟在市场营销中的应用[J].预测, 2000(2): 99-102.
[19] Koen Bertles. Agent-based Social Simulation in Markets. Electronic Commerce Research[J], 2001(1): 149-158.
[20] Oliver Wendt, Falk von Westarp. Determinants of Diffusion in Network Effect Markets[J]. IRMA Conference 2000: 819-823. URL:http://www.wiiw.de/ publikationen/Determinants of Diffusion in Netwo.pdf.
[21] S A. Delre, W. Jager, M A. Janssen. Diffusion Dynamics in Small-World Networks with Heterogeneous Consumers[J]. Computational & Mathematical Organization Theory, 2006(6):1-18.
[22]段文奇,陈忠,陈晓荣.基于复杂网络的新产品赠样目标优化策略[J].系统工程理论与实践, 2006(9): 77-82.
[23] T. Antal, P L. Krapivsky, S. Redner. Dynamics of Social Balance on Networks[J]. Physical Review E 2005, 72(036121):1-10.
[24] D J. Watts, P S. Dodds, M E J. Newman. Identity and Search in Social Networks[J]. Science, 2006(296): 1302-1305.
[25] K. Sun, X M. Mao, Q. Ouyang. Social Influence in Small-World Networks[J]. Chinese Physics, 2002, 11(12): 1280-1285.
[26] M. Kuperman, G. Abramson. Small-World Effect in an Epidemiological Model[J]. Phys. Rev. Lett., 2001, 86(13): 2909-2912.
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