Naming Game Dynamics on Pairs of Connected Networks with Competing Opinions

We study the Naming Game (NG) dynamics when two disjoint networks with nodes in consensus on competing opinions are connected with new links. We consider two sets of networks; one contains several networks with real-life communities, the other networks generated with the Watts-Strogatz and Barabasi-Albert models. For each set, we run NG on all the possible pairs of networks and observe whether a consensus is reached to determine network features that correlate highly with such outcome. The main conclusion is that the quality of network community structure informs network’s ability to resist or exert influence from/on others. Moreover, the outcomes depend on whether Speaker-First of Listener-First NG is run and on whether a speaker or listener is biased towards high or low degree nodes. The results reveal strategies that may be used to enable and accelerate convergence to consensus in social networks.

[1]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[2]  Boleslaw K. Szymanski,et al.  The Naming Game in Social Networks : Community Formation and Consensus Engineering , 2009 .

[3]  Vittorio Loreto,et al.  Nonequilibrium dynamics of language games on complex networks. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[4]  P. Clifford,et al.  A model for spatial conflict , 1973 .

[5]  Frank M. Bass,et al.  A New Product Growth for Model Consumer Durables , 2004, Manag. Sci..

[6]  F. Bass.  A new product growth model for consumer durables , 1976 .

[7]  W. Zachary,et al.  An Information Flow Model for Conflict and Fission in Small Groups , 1977, Journal of Anthropological Research.

[8]  J. Carr.  Applications of Centre Manifold Theory , 1981 .

[9]  David Lusseau,et al.  The emergent properties of a dolphin social network , 2003, Proceedings of the Royal Society of London. Series B: Biological Sciences.

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

[11]  M. Newman,et al.  The structure of scientific collaboration networks. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[12]  Mark S. Granovetter.  Threshold Models of Collective Behavior , 1978, American Journal of Sociology.

[13]  Juli'an Candia,et al.  Mass media influence spreading in social networks with community structure , 2008, 0806.1762.

[14]  Alan J. Miller,et al.  leaps: Regression Subset Selection. , 2004 .

[15]  J. Candia,et al.  Effects Of Mass Media And Cultural Drift In A Model For Social Influence , 2006, physics/0611189.

[16]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[17]  Boleslaw K. Szymanski,et al.  A New Metric for Quality of Network Community Structure , 2015, ArXiv.

[18]  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.

[19]  Luc Steels.  A self-organizing spatial vocabulary , 1995 .

[20]  T. Schelling.  Micromotives and Macrobehavior , 1978 .

[21]  M. Faloutsos.  The internet AS-level topology: three data sources and one definitive metric , 2006, CCRV.

[22]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[23]  Boleslaw K. Szymanski,et al.  Community Detection via Maximization of Modularity and Its Variants , 2014, IEEE Transactions on Computational Social Systems.