Network Feature and the Intervention Performance
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- 英文论文题名：Network Feature and the Intervention Performance
- 论文作者：Caiyun Wang ; Huawei Han ; Jing Han
- 英文论文作者：Caiyun Wang ; Huawei Han ; Jing Han ; Key Laboratory of Systems and Control ; Institute of Systems Science ; Academy of Mathematics and Systems Science ; Chinese Academy of Sciences ; School of Mathematical Sciences ; University of Chinese Academy of Sciences
- 年：2017
- 作者机构：Key Laboratory of Systems and Control,Institute of Systems Science,Academy of Mathematics and Systems Science,Chinese Academy of Sciences;School of Mathematical Sciences,University of Chinese Academy of Sciences;
- 英文论文关键词：network differential degree ; soft control ; intervention performance ; weighted DeGroot model ; convergent opinion
- 会议召开时间：2017-07-26
- 会议录名称：第36届中国控制会议论文集（A）
- 英文会议录名称：Proceedings of the 36th Chinese Control Conference（A）
- 语种：英文
- 分类号：O231
- 学会代码：KZLL
- 会议名称：第36届中国控制会议
- 会议地点：中国辽宁大连
- 主办单位：中国自动化学会控制理论专业委员会
- 学会名称：中国自动化学会控制理论专业委员会
- 页数：6
- 文件大小：407k
- 原文格式：D
- 会议级别：全国
摘要
We apply soft control method on an opinion dynamic model, the weighted De Groot model, to change the convergent opinion value . The interaction network plays an important role in the dynamics of system, and the soft control performance(Δ, the difference between the new convergent opinion value and the original convergent opinion value '). In this paper, we define a new network feature Ω, called ‘network differential degree', to measure how node degrees couple with influential values in the network, i.e., large Ω indicates nodes with high degree is more likely to couple with large influential value. We investigate the relationship between the soft control performance and the network differential degree Ω in the following three intervention scenarios:(1) add one special agent(shill) to connect to one normal agent to achieve the best soft control performance which can maimize |Δ|;(2) add one edge between two normal agents which can maimize |Δ|;(3) add a number of edges among agents which can maimize |Δ|. Through simulations we find significant correlation between |Δ*|(the maimum value of |Δ|)and Ω in all three scenarios: the smaller Ω is, the larger |Δ*| is. So Ω could be used to predict how difficult it is to intervene and change the convergent opinion value of the weighted Degroot model by soft control intervention method. Meanwhile, a theorem of adding one edge and an algorithm for adding optimal edges are given.
We apply soft control method on an opinion dynamic model, the weighted De Groot model, to change the convergent opinion value . The interaction network plays an important role in the dynamics of system, and the soft control performance(Δ, the difference between the new convergent opinion value and the original convergent opinion value '). In this paper, we define a new network feature Ω, called ‘network differential degree', to measure how node degrees couple with influential values in the network, i.e., large Ω indicates nodes with high degree is more likely to couple with large influential value. We investigate the relationship between the soft control performance and the network differential degree Ω in the following three intervention scenarios:(1) add one special agent(shill) to connect to one normal agent to achieve the best soft control performance which can maimize |Δ|;(2) add one edge between two normal agents which can maimize |Δ|;(3) add a number of edges among agents which can maimize |Δ|. Through simulations we find significant correlation between |Δ*|(the maimum value of |Δ|)and Ω in all three scenarios: the smaller Ω is, the larger |Δ*| is. So Ω could be used to predict how difficult it is to intervene and change the convergent opinion value of the weighted Degroot model by soft control intervention method. Meanwhile, a theorem of adding one edge and an algorithm for adding optimal edges are given.
We apply soft control method on an opinion dynamic model, the weighted De Groot model, to change the convergent opinion value . The interaction network plays an important role in the dynamics of system, and the soft control performance(Δ, the difference between the new convergent opinion value and the original convergent opinion value '). In this paper, we define a new network feature Ω, called ‘network differential degree', to measure how node degrees couple with influential values in the network, i.e., large Ω indicates nodes with high degree is more likely to couple with large influential value. We investigate the relationship between the soft control performance and the network differential degree Ω in the following three intervention scenarios:(1) add one special agent(shill) to connect to one normal agent to achieve the best soft control performance which can maimize |Δ|;(2) add one edge between two normal agents which can maimize |Δ|;(3) add a number of edges among agents which can maimize |Δ|. Through simulations we find significant correlation between |Δ*|(the maimum value of |Δ|)and Ω in all three scenarios: the smaller Ω is, the larger |Δ*| is. So Ω could be used to predict how difficult it is to intervene and change the convergent opinion value of the weighted Degroot model by soft control intervention method. Meanwhile, a theorem of adding one edge and an algorithm for adding optimal edges are given.
引文
[1]J.R.French Jr,A formal theory of social power,Psychological Review 63,181–194,1956.
[2]M.H.De Groot,Reaching a consensus,Journal of American Statistical Association,69,118–121,1974.
[3]N.E.Friedkin and E.C.Johnsen,Social influence networks and opinion change,Advances in Group Processes,16,1-29,1999.
[4]U.Krause,Soziale Dynamiken mit vielen Interakteuren,The Journal of Artificial Societies and Social Simulation,01,37–51,1997.
[5]R.Hegselmann and U.Krause,Opinion dynamics and bounded confidence models,analysis,and simulation,Journal of Artificial Societies and Social Simulation,5(3),2002.
[6]G.Deffuant,D.Neau,F.Amblard and G.Wisbuch,Mixing beliefs among interacting agents,Advances in Complex Systems,3,2000.
[7]R.A.Holley and T.M.Liggett,Ergodic theorems for weakly interacting infinite systems and the voter model,Annals of probability,3,643-663,1975.
[8]S.Galam,Sociophysics:a mean behavior model for the process of strike,The Journal of Mathematical Sociology,9,1982.
[9]K.Sznajd-Weron and J.Sznajd,Opinion evolution in closed community,International Journal of Modern Physics C,11(6),1157-1165,2000.
[10]R.Hegselmann and U.Krause,Truth and cognitive division of labour first steps towards a computer aided social epistemology,Journal of Artificial Societies and Social Simulation,2006.
[11]B.Golub and M.O.Jackson,Naive learning in social networks and the wisdom of crowds,American Economic Journal:Microeconomics,Microecon,2(1),112–149,2007.
[12]A.G.Chandrasekhar,H.Rreguy and J.P.Xandari,Testing
models of social learning on networks-evidence from a lab experiment in the field,National Bureau of Economic Research,2015.
[13]M.O.Jackson,Social and economic networks,Princeton University Press,2008.
[14]J.Han,L.Guo and M.Li,Guiding a group of locally interacting autonomous mobile agents,Proceedings of the 24th Chinese Control Conference,184–187,2005.
[15]H.Han,C.Qiang,C.Wang and J.Han,Intervention of DeGroot model by Soft Control,Proceedings of the 34th Chinese Control Conference,1291-1296,2015.
[16]H.Han,C.Qiang,C.Wang and J.Han,Intervention of DeGroot model by Soft Control,Recepted by Journal of Systems Science and Complexity.
[17]H.Han,Soft control of collective opinion based on De Groot model(in Chinese),Phd Thesis,Beijing:Academy of Mathematics and Systems Science,Chinese Academy of Sciences,2015.
[18]J.Han,M.Li,and L.Guo,Soft control on collective behavior of a group of autonomous agents by a shill agent,Journal of Systems Science and Complexity,19(1):54-62,2006.
[19]J.Han and L.Wang,Nondestructive intervention to multiagent systems through an intelligent agent,PLo S ONE,8(5):e61542,2013.
[20]X.Wang,J.Han and H.Han,Special agents can promote cooperation in population,PLo S ONE,6(12):e29182,2011.
[21]H.Duan and C.Sun,Swarm intelligence inspired shills and the evolution of cooperation,Scientific Reports,4,2014.
[22]T.Yu-Song,A.O.Sousa,K.Ling-Jiang and L.Mu-Ren,Sznajd model with synchronous updating on complex networks,ar Xiv preprint cond-mat/0501629,2005.
[23]S.Fortunato,On the consensus threshold for the opinion dynamics of Krause CHegselmann,International Journal of Modern Physics C,16(02),259-270,2005.
[24]G.Deffuant,F.Amblard,G.Weisbuch and T.Faure,How can extremism prevail?A study based on the relative agreement interaction model,Journal of artificial societies and social simulation,5(4),2002.
[25]F.Amblard and G.Deffuant,The role of network topology on extremism propagation with the relative agreement opinion dynamics,Physica A:Statistical Mechanics and its Applications,343,725-738,2004.
[26]M.Kuperman and D.Zanette,Stochastic resonance in a model of opinion formation on small-world networks,The European Physical Journal B-Condensed Matter and Complex Systems,26(3),387-391,2002.
[27]C.Castellano,D Vilone and A.Vespignani,Incomplete ordering of the voter model on small-world networks,EPL(Europhysics Letters),63(1),153,2003.
[28]A.J.Bray,Theory of phase-ordering kinetics,Advances in Physics,51(2),481-587,2002.
[29]M.Cha,H.Haddadi,F.Benevenuto and P.K.Gummadi,Measuring user influence in twitter:The million follower fallacy,Icwsm,10(10-17),30,2010.
[30]H.Kwak,C.Lee,H.Park and S.Moon,What is twitter,a social network or a news media?ACM,591C600,2010.
[31]J.Weng,E.P.Lim,J.Jiang and Q.He,Twitterrank:finding topic-sensitive influential twitterers,ACM,261C270,2010.
[32]E.Seneta,Non-negative matrices and Markov chains,Springer Science&Business Media,2006.
[33]P.Erd?os and A.Rényi,On random graphs,I,Publicationes Mathematicae(Debrecen),6,290–297,1959.
[34]A.Barabsi and R.Albert,Emergence of scaling in random networks,Science,286(5439),509–512,1999.
[35]D.J,Watts and S.H.Strogatz,Collective dynamics of‘small–world’networks,Nature,393(6684),440-442,1998.
[2]M.H.De Groot,Reaching a consensus,Journal of American Statistical Association,69,118–121,1974.
[3]N.E.Friedkin and E.C.Johnsen,Social influence networks and opinion change,Advances in Group Processes,16,1-29,1999.
[4]U.Krause,Soziale Dynamiken mit vielen Interakteuren,The Journal of Artificial Societies and Social Simulation,01,37–51,1997.
[5]R.Hegselmann and U.Krause,Opinion dynamics and bounded confidence models,analysis,and simulation,Journal of Artificial Societies and Social Simulation,5(3),2002.
[6]G.Deffuant,D.Neau,F.Amblard and G.Wisbuch,Mixing beliefs among interacting agents,Advances in Complex Systems,3,2000.
[7]R.A.Holley and T.M.Liggett,Ergodic theorems for weakly interacting infinite systems and the voter model,Annals of probability,3,643-663,1975.
[8]S.Galam,Sociophysics:a mean behavior model for the process of strike,The Journal of Mathematical Sociology,9,1982.
[9]K.Sznajd-Weron and J.Sznajd,Opinion evolution in closed community,International Journal of Modern Physics C,11(6),1157-1165,2000.
[10]R.Hegselmann and U.Krause,Truth and cognitive division of labour first steps towards a computer aided social epistemology,Journal of Artificial Societies and Social Simulation,2006.
[11]B.Golub and M.O.Jackson,Naive learning in social networks and the wisdom of crowds,American Economic Journal:Microeconomics,Microecon,2(1),112–149,2007.
[12]A.G.Chandrasekhar,H.Rreguy and J.P.Xandari,Testing
models of social learning on networks-evidence from a lab experiment in the field,National Bureau of Economic Research,2015.
[13]M.O.Jackson,Social and economic networks,Princeton University Press,2008.
[14]J.Han,L.Guo and M.Li,Guiding a group of locally interacting autonomous mobile agents,Proceedings of the 24th Chinese Control Conference,184–187,2005.
[15]H.Han,C.Qiang,C.Wang and J.Han,Intervention of DeGroot model by Soft Control,Proceedings of the 34th Chinese Control Conference,1291-1296,2015.
[16]H.Han,C.Qiang,C.Wang and J.Han,Intervention of DeGroot model by Soft Control,Recepted by Journal of Systems Science and Complexity.
[17]H.Han,Soft control of collective opinion based on De Groot model(in Chinese),Phd Thesis,Beijing:Academy of Mathematics and Systems Science,Chinese Academy of Sciences,2015.
[18]J.Han,M.Li,and L.Guo,Soft control on collective behavior of a group of autonomous agents by a shill agent,Journal of Systems Science and Complexity,19(1):54-62,2006.
[19]J.Han and L.Wang,Nondestructive intervention to multiagent systems through an intelligent agent,PLo S ONE,8(5):e61542,2013.
[20]X.Wang,J.Han and H.Han,Special agents can promote cooperation in population,PLo S ONE,6(12):e29182,2011.
[21]H.Duan and C.Sun,Swarm intelligence inspired shills and the evolution of cooperation,Scientific Reports,4,2014.
[22]T.Yu-Song,A.O.Sousa,K.Ling-Jiang and L.Mu-Ren,Sznajd model with synchronous updating on complex networks,ar Xiv preprint cond-mat/0501629,2005.
[23]S.Fortunato,On the consensus threshold for the opinion dynamics of Krause CHegselmann,International Journal of Modern Physics C,16(02),259-270,2005.
[24]G.Deffuant,F.Amblard,G.Weisbuch and T.Faure,How can extremism prevail?A study based on the relative agreement interaction model,Journal of artificial societies and social simulation,5(4),2002.
[25]F.Amblard and G.Deffuant,The role of network topology on extremism propagation with the relative agreement opinion dynamics,Physica A:Statistical Mechanics and its Applications,343,725-738,2004.
[26]M.Kuperman and D.Zanette,Stochastic resonance in a model of opinion formation on small-world networks,The European Physical Journal B-Condensed Matter and Complex Systems,26(3),387-391,2002.
[27]C.Castellano,D Vilone and A.Vespignani,Incomplete ordering of the voter model on small-world networks,EPL(Europhysics Letters),63(1),153,2003.
[28]A.J.Bray,Theory of phase-ordering kinetics,Advances in Physics,51(2),481-587,2002.
[29]M.Cha,H.Haddadi,F.Benevenuto and P.K.Gummadi,Measuring user influence in twitter:The million follower fallacy,Icwsm,10(10-17),30,2010.
[30]H.Kwak,C.Lee,H.Park and S.Moon,What is twitter,a social network or a news media?ACM,591C600,2010.
[31]J.Weng,E.P.Lim,J.Jiang and Q.He,Twitterrank:finding topic-sensitive influential twitterers,ACM,261C270,2010.
[32]E.Seneta,Non-negative matrices and Markov chains,Springer Science&Business Media,2006.
[33]P.Erd?os and A.Rényi,On random graphs,I,Publicationes Mathematicae(Debrecen),6,290–297,1959.
[34]A.Barabsi and R.Albert,Emergence of scaling in random networks,Science,286(5439),509–512,1999.
[35]D.J,Watts and S.H.Strogatz,Collective dynamics of‘small–world’networks,Nature,393(6684),440-442,1998.