Majority-vote dynamics on multiplex networks with two layers

Majority-vote model is a much-studied model for social opinion dynamics of two competing opinions. With the recent appreciation that our social network comprises a variety of different 'layers' forming a multiplex network, a natural question arises on how such multiplex interactions affect the social opinion dynamics and consensus formation. Here, the majority-vote processes will be studied on multiplex networks with two layers to understand the effect of multiplexity on opinion dynamics. We will discuss how global consensus is reached by different types of voters: AND- and OR-rule voters on multiplex-network and voters on single-network system. The AND-model reaches the largest consensus below the critical noise parameter q c . It needs, however, much longer time to reach consensus than other models. In the vicinity of the transition point, the consensus collapses abruptly. The OR-model attains smaller level of consensus than the AND-rule but reaches the consensus more quickly. Its consensus transition is continuous. The numerical simulation results are supported qualitatively by analytical calculations based on the approximate master equation.

[1]  W. Hager,et al.  and s , 2019, Shallow Water Hydraulics.

[2]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[3]  Jung Yeol Kim,et al.  Correlated multiplexity and connectivity of multiplex random networks , 2011, 1111.0107.

[4]  B Kahng,et al.  Ashkin-Teller model and diverse opinion phase transitions on multiplex networks. , 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.

[5]  Byungjoon Min,et al.  Layer-switching cost and optimality in information spreading on multiplex networks , 2013, Scientific Reports.

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

[7]  J. Gleeson,et al.  Seed size strongly affects cascades on random networks. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[8]  S. Galam Sociophysics: A Physicist's Modeling of Psycho-political Phenomena , 2012 .

[9]  K-I Goh,et al.  Multiple resource demands and viability in multiplex networks. , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.

[10]  S. Fortunato,et al.  Statistical physics of social dynamics , 2007, 0710.3256.

[11]  Sergio Gómez,et al.  On the dynamical interplay between awareness and epidemic spreading in multiplex networks , 2013, Physical review letters.

[12]  P. Alam ‘N’ , 2021, Composites Engineering: An A–Z Guide.

[13]  M. J. Oliveira,et al.  Isotropic majority-vote model on a square lattice , 1992 .

[14]  Nikos E. Kouvaris,et al.  Opinion competition dynamics on multiplex networks , 2017 .

[15]  Katarzyna Sznajd-Weron,et al.  Phase transitions in the q-voter model with noise on a duplex clique. , 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.

[16]  Krzysztof Suchecki,et al.  Bistable-monostable transition in the Ising model on two connected complex networks. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[17]  Miss A.O. Penney (b) , 1974, The New Yale Book of Quotations.

[18]  Serge Gallam Majority rule, hierarchical structures, and democratic totalitarianism: a statistical approach , 1986 .

[19]  K-I Goh,et al.  Multiplexity-facilitated cascades in networks. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[20]  P. Alam ‘A’ , 2021, Composites Engineering: An A–Z Guide.

[21]  T. Liggett Interacting Particle Systems , 1985 .

[22]  F. Moreira,et al.  Small-world effects in the majority-vote model. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[23]  Peter Grassberger,et al.  Percolation theory on interdependent networks based on epidemic spreading , 2011, 1109.4447.

[24]  K-I Goh,et al.  Threshold cascades with response heterogeneity in multiplex networks. , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.

[25]  J. Gleeson Binary-state dynamics on complex networks: pair approximation and beyond , 2012, 1209.2983.

[26]  Grzegorz Siudem,et al.  Majority Vote Model on Multiplex Networks , 2018 .

[27]  R. Sarpong,et al.  Bio-inspired synthesis of xishacorenes A, B, and C, and a new congener from fuscol† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c9sc02572c , 2019, Chemical science.

[28]  Mason A. Porter,et al.  Multilayer networks , 2013, J. Complex Networks.

[29]  Ginestra Bianconi,et al.  Mutually connected component of networks of networks with replica nodes. , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.

[30]  F. Moreira,et al.  Majority-vote model on random graphs. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[31]  Kwang-Il Goh,et al.  Towards real-world complexity: an introduction to multiplex networks , 2015, ArXiv.

[32]  Beom Jun Kim,et al.  Synchronization in interdependent networks. , 2011, Chaos.

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

[34]  Antonio Scala,et al.  Networks of Networks: The Last Frontier of Complexity , 2014 .

[35]  Sergey N. Dorogovtsev,et al.  Weak percolation on multiplex networks , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[36]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[37]  Thomas de Quincey [C] , 2000, The Works of Thomas De Quincey, Vol. 1: Writings, 1799–1820.

[38]  J. Kurths,et al.  First-order phase transition in a majority-vote model with inertia. , 2017, Physical Review E.