Topology Reconstruction of Complex Networks with Time-varying Parameters Nodes
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- 英文论文题名：Topology Reconstruction of Complex Networks with Time-varying Parameters Nodes
- 论文作者：Xiong Wang ; Qianyao Wang ; Jinhu Lü
- 英文论文作者：Xiong Wang ; Qianyao Wang ; Jinhu Lü ; Key Laboratory of Systems and Control ; Academy of Mathematics and Systems Science ; Chinese Academy of Sciences
- 年：2017
- 作者机构：Key Laboratory of Systems and Control,Academy of Mathematics and Systems Science,Chinese Academy of Sciences;
- 英文论文关键词：Complex Networks ; time-varying node dynamical parameters ; Lasso method ; reconstruction networks
- 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：O157.5
- 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：5
- 文件大小：191k
- 原文格式：D
- 会议级别：全国
摘要
Complex networks with time-varying parameters nodes are of considerable interest in many areas of science, engineering and nature. Reconstructing networks with unknown but bounded time-varying parameters nodes from limited measured information is desirable and of significant interest for using and controlling these networks. Based on the Lasso method and the Taylor expansion approximation, we develop an efficient, feasible, and completely data-driven approach to predicting the structures of the networks with unknown time-varying parameters nodes in present of noise or without noise. In particular, through the numerical simulations, we demonstrate that, networks structures can be fully reconstructed even only few information available under the conditions of the systemic parameter is time-varying and in the presence of noise, and this method is effective and robust. We expect our method to be useful in addressing issues of significantly current concern in the information era and natural networks.
Complex networks with time-varying parameters nodes are of considerable interest in many areas of science, engineering and nature. Reconstructing networks with unknown but bounded time-varying parameters nodes from limited measured information is desirable and of significant interest for using and controlling these networks. Based on the Lasso method and the Taylor expansion approximation, we develop an efficient, feasible, and completely data-driven approach to predicting the structures of the networks with unknown time-varying parameters nodes in present of noise or without noise. In particular, through the numerical simulations, we demonstrate that, networks structures can be fully reconstructed even only few information available under the conditions of the systemic parameter is time-varying and in the presence of noise, and this method is effective and robust. We expect our method to be useful in addressing issues of significantly current concern in the information era and natural networks.
Complex networks with time-varying parameters nodes are of considerable interest in many areas of science, engineering and nature. Reconstructing networks with unknown but bounded time-varying parameters nodes from limited measured information is desirable and of significant interest for using and controlling these networks. Based on the Lasso method and the Taylor expansion approximation, we develop an efficient, feasible, and completely data-driven approach to predicting the structures of the networks with unknown time-varying parameters nodes in present of noise or without noise. In particular, through the numerical simulations, we demonstrate that, networks structures can be fully reconstructed even only few information available under the conditions of the systemic parameter is time-varying and in the presence of noise, and this method is effective and robust. We expect our method to be useful in addressing issues of significantly current concern in the information era and natural networks.
引文
[1]G.Chen and J.Lü,Dynamics of the Lorenz System Family:Analysis,Control and Synchronization,Beijing,Science Press,2003.
[2]H.K.Khalil,Nonlinear Systems,3rd ed.,Amazon:Upper Saddle River,NJ:Prentice-Hall,2002.
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[8]X.Wu,W.Wang,and W.X.Zheng,“Inferring topologies of complex networks with hidden variables,”Phys.Rev.E,vol.86,p.046106,2012.
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[12]Z.Shen,W.Wang,Y.Fan,Z.Di,and Y.C.Lai,“Reconstructing propagation networks with natural diversity and identifying hidden sources,”Nat.Commun.,vol.5,no.4323,pp.1–10,2014.
[13]X.Han,Z.Shen,W.Wang,and Z.Di,“Robust reconstruction of complex networks from sparse data,”Phys.Rev.Lett.,vol.114,p.028701,2015.
[14]G.Li,X.Wu,J.Liu,J.Lu,and C.Guo,“Recovering network topologies via Taylor expansion and compressive sensing,”Chaos,vol.25,p.043102,2015.
[15]T.Hastie,R.Tibshirani,J.Friedman,and J.Franklin,“The elements of statistical learning:data mining,inference and prediction,”Math.Intell.,vol.27,pp.83–85,2005.
[16]X.Yu,“Variable structure control approach for controlling chaos,”Chaos Soliton.Fract.,vol.8,no.9,pp.1577–1586,1997.
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[2]H.K.Khalil,Nonlinear Systems,3rd ed.,Amazon:Upper Saddle River,NJ:Prentice-Hall,2002.
[3]S.H.Strogatz,“Exploring complex networks,”Nature,vol.410,pp.268–276,2001.
[4]D.J.Watts and S.H.Strogatz,“Collective dynamics of’small world’networks,”Nature,vol.393,pp.440–442,1998.
[5]K.Zhou,J.C.Doyle,and K.Glover,Robust and Optimal Control,Prentice Hall,New Jersey,1996.
[6]J.Lü and G.Chen,“A time-varying complex dynamical network model and its controlled synchronization criteria,”IEEE Trans.Autom.Contr.,vol.50,no.6,pp.841–846,2005.
[7]R.Jansen,H.Yu,D.Greenbaum,Y.Kluger,N.J.Krogan,S.Chung,A.Emili,M.Snyder,J.F.Greenblatt,and M.Gerstein,“A Bayesian networks approach for predicting proteinprotein interactions from genomic data,”Science,vol.302,no.5644,pp.449–453,2003.
[8]X.Wu,W.Wang,and W.X.Zheng,“Inferring topologies of complex networks with hidden variables,”Phys.Rev.E,vol.86,p.046106,2012.
[9]H.Liu,J.Lu,J.Lü,and D.J.Hill,“Structure identification of uncertain general complex dynamical networks with time delay,”Automatica,vol.45,no.8,pp.1799–1807,2009.
[10]X.Wu,C.Zhou,G.Chen,and J.Lu,“Detecting the topologies of complex networks with stochastic perturbations,”Chaos,vol.21,p.043129,2011.
[11]W.X.Wang,R.Yang,Y.C.Lai,V.Kovaanis,and C.Grebogi,“Predicting catastrophes in nonlinear dynamical systems by compressive sensing,”Phys.Rev.Lett.,vol.106,p.154101,2011.
[12]Z.Shen,W.Wang,Y.Fan,Z.Di,and Y.C.Lai,“Reconstructing propagation networks with natural diversity and identifying hidden sources,”Nat.Commun.,vol.5,no.4323,pp.1–10,2014.
[13]X.Han,Z.Shen,W.Wang,and Z.Di,“Robust reconstruction of complex networks from sparse data,”Phys.Rev.Lett.,vol.114,p.028701,2015.
[14]G.Li,X.Wu,J.Liu,J.Lu,and C.Guo,“Recovering network topologies via Taylor expansion and compressive sensing,”Chaos,vol.25,p.043102,2015.
[15]T.Hastie,R.Tibshirani,J.Friedman,and J.Franklin,“The elements of statistical learning:data mining,inference and prediction,”Math.Intell.,vol.27,pp.83–85,2005.
[16]X.Yu,“Variable structure control approach for controlling chaos,”Chaos Soliton.Fract.,vol.8,no.9,pp.1577–1586,1997.
[17]D.Marbach,J.C.Costello,R.Küffner,N.M.Vega,R.J.Prill,D.M.Camacho,K.R.Allison,The DREAM5 Consortium,M.Kellis,J.J.Collins,and G.Stolovitzky,“Wisdom of crowds for robust gene network inference,”Nat.Methods,vol.9,pp.796–804,2012.