Discrete opinion dynamics on networks based on social influence

A model of opinion dynamics based on social influence on networks was studied. The opinion of each agent can have integer values i = 1, 2, ..., I and opinion exchanges are restricted to connected agents. It was found that for any I ≥ 2 and self-confidence parameter 0 ≤ u < 1, when u is a degree-independent constant, the weighted proportion qi of the population that hold a given opinion i is a martingale, and the fraction qi of opinion i will gradually converge to qi. The tendency can slow down with the increase of degree assortativity of networks. When u is degree dependent, qi does not possess the martingale property, however qi still converges to it. In both cases for a finite network the states of all agents will finally reach consensus. Further if there exist stubborn persons in the population whose opinions do not change over time, it was found that for degree-independent constant u, both qi and qi will converge to fixed proportions which only depend on the distribution of initial obstinate persons, and naturally the final equilibrium state will be the coexistence of diverse opinions held by the stubborn people. The analytical results were verified by numerical simulations on Barabasi–Albert (BA) networks. The model highlights the influence of high-degree agents on the final consensus or coexistence state and captures some realistic features of the diffusion of opinions in social networks.

[1]  Serge Galam,et al.  Real space renormalization group and totalitarian paradox of majority rule voting , 2000 .

[2]  G. Caldarelli,et al.  Assortative model for social networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[3]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[4]  Petter Holme,et al.  Structure and time evolution of an Internet dating community , 2002, Soc. Networks.

[5]  S. Galam Heterogeneous beliefs, segregation, and extremism in the making of public opinions. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  S. Galam Application of statistical physics to politics , 1999, cond-mat/0004306.

[7]  Serge Galam,et al.  Sociophysics: a personal testimony , 2004, physics/0403122.

[8]  S. Redner,et al.  Dynamics of majority rule in two-state interacting spin systems. , 2003, Physical review letters.

[9]  Philip Ball,et al.  The physical modelling of society: a historical perspective , 2002 .

[10]  S. Redner,et al.  Voter models on heterogeneous networks. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[11]  M. Aliev,et al.  Measurement of $K^+ \to \pi^0 \pi^0 e^+ \nu$ ($K_{e4}^{00}$) decay using stopped positive kaons , 2004, hep-ex/0408098.

[12]  A. Bunde,et al.  Propagation of confidential information on scale-free networks , 2007 .

[13]  Clelia M. Bordogna,et al.  Statistical methods applied to the study of opinion formation models: a brief overview and results of a numerical study of a model based on the social impact theory , 2007 .

[14]  A-L Barabási,et al.  Structure and tie strengths in mobile communication networks , 2006, Proceedings of the National Academy of Sciences.

[15]  D. Stauffer,et al.  SIMULATION OF CONSENSUS MODEL OF DEFFUANT et al. ON A BARABÁSI–ALBERT NETWORK , 2004 .

[16]  Alain Barrat,et al.  Who's talking first? Consensus or lack thereof in coevolving opinion formation models. , 2007, Physical review letters.

[17]  Krapivsky,et al.  Exact results for kinetics of catalytic reactions. , 1996, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[18]  R. Axelrod The Dissemination of Culture , 1997 .

[19]  Exact results for two-dimensional coarsening , 2008 .

[20]  M. Scheucher,et al.  A soluble kinetic model for spinodal decomposition , 1988 .

[21]  T. Liggett,et al.  Stochastic Interacting Systems: Contact, Voter and Exclusion Processes , 1999 .

[22]  Armin Bunde,et al.  On the spreading and localization of risky information in social networks , 2007 .

[23]  Guillaume Deffuant,et al.  Mixing beliefs among interacting agents , 2000, Adv. Complex Syst..

[24]  Pedro G. Lind,et al.  The spread of gossip in American schools , 2007 .

[25]  Gergely Kocsis,et al.  The effect of network topologies on the spreading of technological developments , 2008 .

[26]  Renaud Lambiotte,et al.  Majority rule on heterogeneous networks , 2008 .

[27]  F. Vazquez,et al.  Analytical solution of the voter model on uncorrelated networks , 2008, 0803.1686.

[28]  V Schwämmle,et al.  Different topologies for a herding model of opinion. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[29]  Katarzyna Sznajd-Weron,et al.  Opinion evolution in closed community , 2000, cond-mat/0101130.

[30]  Krzysztof Suchecki,et al.  Voter model dynamics in complex networks: Role of dimensionality, disorder, and degree distribution. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[31]  Maxi San Miguel,et al.  Generic absorbing transition in coevolution dynamics. , 2007, Physical review letters.

[32]  Pedro G. Lind,et al.  Networks based on collisions among mobile agents , 2006 .

[33]  S. N. Dorogovtsev,et al.  Potts model on complex networks , 2004 .

[34]  F. Schweitzer,et al.  SOCIAL IMPACT MODELS OF OPINION DYNAMICS , 2001 .

[35]  Xiaofan Wang,et al.  Evolution of a large online social network , 2009 .

[36]  F. Y. Wu The Potts model , 1982 .

[37]  S. Solomon,et al.  Social percolation models , 1999, adap-org/9909001.

[38]  Guillaume Deffuant,et al.  Meet, discuss, and segregate! , 2002, Complex..

[39]  Gerard Weisbuch Bounded confidence and social networks , 2004 .

[40]  M. Newman Coauthorship networks and patterns of scientific collaboration , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[41]  Hans J Herrmann,et al.  Spreading gossip in social networks. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[42]  I. Sokolov,et al.  Reshuffling scale-free networks: from random to assortative. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[43]  Heinz Mühlenbein,et al.  Coordination of Decisions in a Spatial Agent Model , 2001, ArXiv.

[44]  Serge Galam,et al.  Modelling rumors: the no plane Pentagon French hoax case , 2002, cond-mat/0211571.

[45]  Dirk Helbing,et al.  Simulating dynamical features of escape panic , 2000, Nature.