Diffusion in large networks

We investigate the phenomenon of diffusion in a countably infinite society of individuals interacting with their neighbors in a network. At a given time, each individual is either active or inactive. The diffusion is driven by two characteristics: the network structure and the diffusion mechanism represented by an aggregation function. We distinguish between two diffusion mechanisms (probabilistic, deterministic) and focus on two types of aggregation functions (strict, Boolean). Under strict aggregation functions, polarization of the society cannot happen, and its state evolves towards a mixture of infinitely many active and infinitely many inactive agents, or towards a homogeneous society. Under Boolean aggregation functions, the diffusion process becomes deterministic and the contagion model of Morris (2000) becomes a particular case of our framework. Polarization can then happen. Our dynamics also allows for cycles in both cases. The network structure is not relevant for these questions, but is important for establishing irreducibility, at the price of a richness assumption: the network should contain infinitely many complex stars and have enough space for storing local configurations. Our model can be given a game-theoretic interpretation via a local coordination game, where each player would apply a best-response strategy in a random neighborhood.

[1]  M. Degroot Reaching a Consensus , 1974 .

[2]  R. Holley,et al.  Ergodic Theorems for Weakly Interacting Infinite Systems and the Voter Model , 1975 .

[3]  Alexander Grey,et al.  The Mathematical Theory of Infectious Diseases and Its Applications , 1977 .

[4]  N. Ling The Mathematical Theory of Infectious Diseases and its applications , 1978 .

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

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

[7]  Eric Goles Ch.,et al.  Periodic behaviour of generalized threshold functions , 1980, Discret. Math..

[8]  H. Poincaré,et al.  Percolation ? , 1982 .

[9]  Svatopluk Poljak,et al.  On periodical behaviour in societies with symmetric influences , 1983, Comb..

[10]  S. Redner,et al.  Introduction To Percolation Theory , 2018 .

[11]  A. Banerjee,et al.  A Simple Model of Herd Behavior , 1992 .

[12]  S. Bikhchandani,et al.  You have printed the following article : A Theory of Fads , Fashion , Custom , and Cultural Change as Informational Cascades , 2007 .

[13]  Glenn Ellison Learning, Local Interaction, and Coordination , 1993 .

[14]  A. Banerjee,et al.  The Economics of Rumours , 1993 .

[15]  Glenn Ellison,et al.  Rules of Thumb for Social Learning , 1993, Journal of Political Economy.

[16]  P. Malliavin Infinite dimensional analysis , 1993 .

[17]  L. Blume The Statistical Mechanics of Best-Response Strategy Revision , 1995 .

[18]  G. Moran ON THE PERIOD-TWO-PROPERTY OF THE MAJORITY OPERATOR IN INFINITE GRAPHS , 1995 .

[19]  Glenn Ellison,et al.  Basins of Attraction, Long-Run Equilibria, and the Speed of Step-by-Step Evolution , 1995 .

[20]  D. Griffeath,et al.  Cellular Automaton Growth on Z2: Theorems, Examples, and Problems , 1998 .

[21]  S. Goyal,et al.  Learning from neighbours , 1998 .

[22]  In Ho Lee,et al.  Noisy Contagion Without Mutation , 2000 .

[23]  G. Silverman,et al.  The Secrets of Word-of-Mouth Marketing: How to Trigger Exponential Sales Through Runaway Word of Mouth , 2001 .

[24]  Eli Berger Dynamic Monopolies of Constant Size , 2001, J. Comb. Theory, Ser. B.

[25]  Lada A. Adamic,et al.  Local Search in Unstructured Networks , 2002, ArXiv.

[26]  Duncan J Watts,et al.  A simple model of global cascades on random networks , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[27]  David Peleg,et al.  Local majorities, coalitions and monopolies in graphs: a review , 2002, Theor. Comput. Sci..

[28]  E. Rogers,et al.  Diffusion of innovations , 1964, Encyclopedia of Sport Management.

[29]  Douglas Gale,et al.  Bayesian learning in social networks , 2003, Games Econ. Behav..

[30]  O. Hernández-Lerma,et al.  Markov chains and invariant probabilities , 2003 .

[31]  A. RÉNY,et al.  ON THE EXISTENCE OF A FACTOR OF DEGREE ONE OF A CONNECTED RANDOM GRAPH , 2004 .

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

[33]  Drew Fudenberg,et al.  Word-of-mouth learning , 2004, Games Econ. Behav..

[34]  Heski Bar-Isaac,et al.  How to Organize Crime , 2006 .

[35]  Mariagiovanna Baccara,et al.  How to Organize Crime , 2006 .

[36]  Matthew O. Jackson,et al.  Relating Network Structure to Diffusion Properties through Stochastic Dominance , 2007 .

[37]  M. Jackson,et al.  Diffusion of Behavior and Equilibrium Properties in Network Games , 2007 .

[38]  M. Keeling,et al.  Modeling Infectious Diseases in Humans and Animals , 2007 .

[39]  Dunia López-Pintado,et al.  Diffusion in complex social networks , 2008, Games Econ. Behav..

[40]  Ilan Lobel,et al.  BAYESIAN LEARNING IN SOCIAL NETWORKS , 2008 .

[41]  Fred S. Roberts,et al.  Irreversible k-threshold processes: Graph-theoretical threshold models of the spread of disease and of opinion , 2009, Discret. Appl. Math..

[42]  P. J. Lamberson Social Learning in Social Networks , 2010 .

[43]  Matthew O. Jackson,et al.  Naïve Learning in Social Networks and the Wisdom of Crowds , 2010 .

[44]  Tansu Alpcan,et al.  Network Security , 2010 .

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

[46]  Franklin Allen,et al.  Financial Contagion , 2000, Journal of Political Economy.

[47]  Asuman E. Ozdaglar,et al.  Opinion Dynamics and Learning in Social Networks , 2010, Dyn. Games Appl..

[48]  R. Kasprzyk Diffusion in Networks , 2012, Journal of Telecommunications and Information Technology.

[49]  Andrea Galeotti,et al.  Strategic immunization and group structure , 2013 .

[50]  Asuman E. Ozdaglar,et al.  Opinion Fluctuations and Disagreement in Social Networks , 2010, Math. Oper. Res..

[51]  Michel Grabisch,et al.  Anonymous Social Influence , 2013, Games Econ. Behav..

[52]  A. Tahbaz-Salehi,et al.  Systemic Risk and Stability in Financial Networks , 2013 .

[53]  Matthew O. Jackson,et al.  Diffusion and contagion in networks with heterogeneous agents and homophily , 2011, Network Science.

[54]  Michel Grabisch,et al.  A model of influence based on aggregation functions , 2013, Math. Soc. Sci..

[55]  Arthur Campbell Word-of-Mouth Communication and Percolation in Social Networks † , 2013 .

[56]  M. Elliott,et al.  Financial Networks and Contagion , 2014 .

[57]  Antonio Cabrales,et al.  Risk-Sharing and Contagion in Networks , 2014, SSRN Electronic Journal.

[58]  S. Goyal,et al.  Attack, Defence, and Contagion in Networks , 2014 .

[59]  Imogen Cara Halstead,et al.  Learning in social networks , 2014 .

[60]  Alireza Tahbaz-Salehi,et al.  Networks, Shocks, and Systemic Risk , 2015 .

[61]  Ruocheng Guo,et al.  Diffusion in Social Networks , 2015, SpringerBriefs in Computer Science.

[62]  Piero Gottardi,et al.  Financial Contagion in Networks , 2015 .

[63]  Éva Tardos,et al.  Maximizing the Spread of Influence through a Social Network , 2015, Theory Comput..

[64]  Brian W. Rogers,et al.  The Oxford handbook of the economics of networks , 2016 .

[65]  Jie Gao,et al.  General Threshold Model for Social Cascades: Analysis and Simulations , 2016, EC.

[66]  Asuman Ozdaglar,et al.  A Simple Model of Cascades in Networks ∗ , 2016 .

[67]  John N. Tsitsiklis,et al.  When Is a Network Epidemic Hard to Eliminate? , 2015, Math. Oper. Res..

[68]  Lijun Bo,et al.  Risk-Sensitive Asset Management and Cascading Defaults , 2017, Math. Oper. Res..

[69]  Gabrielle Demange,et al.  Contagion in Financial Networks: A Threat Index , 2012, Manag. Sci..

[70]  Gabrielle Demange,et al.  Rumors and Social Networks , 2018 .

[71]  Michel Grabisch,et al.  A model of anonymous influence with anti-conformist agents , 2019, Journal of Economic Dynamics and Control.

[72]  Michel Grabisch,et al.  A Survey on Nonstrategic Models of Opinion Dynamics , 2020, Games.