Control Energy and controllability of multilayer Networks

The controllability of multilayer networks has become increasingly important in many areas of science and engineering. In this paper, we identify the general rules that determine the controllability and control energy cost of multilayer networks. First, we quantitatively estimate the control energy cost of multilayer networks and investigate the impacts of different coupling strength and coupling patterns on the control energy cost for multilayer networks. The results indicate that the average energy and the coupling strength have an approximately linear relationship in multilayer networks with two layers. Second, we study how the coupling strength and the connection patterns between different layers affect the controllability of multilayer networks from both theoretical and numerical aspects. The obtained piecewise functional relations between the controllability’s measure and coupling strength reveal the existence of an optimal coupling strength for the different interconnection strategies in multilayer networks. In particular, the numerical experiments demonstrate that there exists a tradeoff between the optimal controllability and optimal control energy for selecting interlayer connection patterns in multilayer networks. These results provide a comprehensive understanding of the impact of interlayer couplings on the controllability and control energy cost for multilayer networks and provide a methodology for selecting the control nodes and coupling strength to maximize the controllability and minimize the control energy cost.

[1]  Albert-László Barabási,et al.  Control Principles of Complex Networks , 2015, ArXiv.

[2]  R Sevilla-Escoboza,et al.  Synchronization of interconnected networks: the role of connector nodes. , 2014, Physical review letters.

[3]  Albert-László Barabási,et al.  Controllability of complex networks , 2011, Nature.

[4]  Pietro Liò,et al.  Systems medicine of inflammaging , 2015, Briefings Bioinform..

[5]  Cleve B. Moler,et al.  Nineteen Dubious Ways to Compute the Exponential of a Matrix, Twenty-Five Years Later , 1978, SIAM Rev..

[6]  Ying-Cheng Lai,et al.  Exact controllability of multiplex networks , 2014 .

[7]  Harry Eugene Stanley,et al.  Robustness of a Network of Networks , 2010, Physical review letters.

[8]  Xiufen Zou,et al.  Complex Dynamical Analysis of a Coupled Network from Innate Immune responses , 2013, Int. J. Bifurc. Chaos.

[9]  Francesco Bullo,et al.  Controllability Metrics, Limitations and Algorithms for Complex Networks , 2014, IEEE Trans. Control. Netw. Syst..

[10]  Fang-Xiang Wu,et al.  Estimation of control Energy and control Strategies for Complex Networks , 2015, Adv. Complex Syst..

[11]  C. D. Johnson,et al.  Optimization of a Certain Quality of Complete Controllability and Observability for Linear Dynamical Systems , 1969 .

[12]  S. P. Cornelius,et al.  Realistic control of network dynamics , 2013, Nature Communications.

[13]  H. Weber,et al.  Analysis and optimization of certain qualities of controllability and observability for linear dynamical systems , 1972 .

[14]  Albert-László Barabási,et al.  Controllability of multiplex, multi-time-scale networks. , 2016, Physical review. E.

[15]  Fang-Xiang Wu,et al.  Domain control of nonlinear networked systems and applications to complex disease networks , 2017 .

[16]  Mark Newman,et al.  Networks: An Introduction , 2010 .

[17]  Jukka-Pekka Onnela,et al.  Community Structure in Time-Dependent, Multiscale, and Multiplex Networks , 2009, Science.

[18]  Wen-Xu Wang,et al.  Exact controllability of complex networks , 2013, Nature Communications.

[19]  Jie Ren,et al.  Controlling complex networks: How much energy is needed? , 2012, Physical review letters.

[20]  Ginestra Bianconi,et al.  Control of Multilayer Networks , 2015, Scientific Reports.

[21]  Harry Eugene Stanley,et al.  Catastrophic cascade of failures in interdependent networks , 2009, Nature.

[22]  Xiufen Zou,et al.  Optimal Control Strategy for Abnormal Innate Immune Response , 2015, Comput. Math. Methods Medicine.

[23]  Conrado J. Pérez Vicente,et al.  Diffusion dynamics on multiplex networks , 2012, Physical review letters.

[24]  Alexander Olshevsky,et al.  Minimal Controllability Problems , 2013, IEEE Transactions on Control of Network Systems.

[25]  Sean R. Davis,et al.  NCBI GEO: archive for functional genomics data sets—update , 2012, Nucleic Acids Res..

[26]  J. Slotine,et al.  Spectrum of controlling and observing complex networks , 2015, Nature Physics.

[27]  Xiufen Zou,et al.  Mathematical modeling for intracellular transport and binding of HIV-1 Gag proteins. , 2015, Mathematical biosciences.

[28]  Derek Ruths,et al.  Control Profiles of Complex Networks , 2014, Science.

[29]  Peng Sun Co-controllability of drug-disease-gene network , 2015 .

[30]  M.L.J. Hautus,et al.  Controllability and observability conditions of linear autonomous systems , 1969 .

[31]  Xiufen Zou,et al.  Deciphering deterioration mechanisms of complex diseases based on the construction of dynamic networks and systems analysis , 2015, Scientific Reports.

[32]  Z. Wang,et al.  The structure and dynamics of multilayer networks , 2014, Physics Reports.

[33]  Yan Zhang,et al.  The value of peripheral nodes in controlling multilayer networks , 2016, Physical review. E.

[34]  Xiufen Zou,et al.  Mathematical Modeling and Nonlinear Dynamical Analysis of Cell Growth in Response to Antibiotics , 2015, Int. J. Bifurc. Chaos.

[35]  A. Arenas,et al.  Abrupt transition in the structural formation of interconnected networks , 2013, Nature Physics.

[36]  Juan Liu,et al.  Transittability of complex networks and its applications to regulatory biomolecular networks , 2014, Scientific Reports.

[37]  Xiufen Zou,et al.  Crosstalk between pathways enhances the controllability of signalling networks. , 2016, IET systems biology.

[38]  Xiufen Zou,et al.  Identifying key nodes in multilayer networks based on tensor decomposition. , 2017, Chaos.

[39]  Yuanyuan Li,et al.  Identifying disease modules and components of viral infections based on multi-layer networks , 2016, Science China Information Sciences.