Extreme Thouless effect in a minimal model of dynamic social networks

TL;DR

This paper demonstrates an extreme Thouless effect in a minimal dynamic social network model, where the system exhibits a sharp phase transition with maximal fluctuations, characterized by a sudden jump in the fraction of cross-group links.

Contribution

The study introduces a minimal model of social networks with extreme introverts and extroverts, revealing an exact stationary distribution and demonstrating an extreme Thouless effect through simulations and mean-field theory.

Findings

Fraction of cross-group links jumps from 0 to 1 at N_I=N_E

All link configurations are equally probable at N_I=N_E

System exhibits a mixed order phase transition with maximal fluctuations

Abstract

In common descriptions of phase transitions, first order transitions are characterized by discontinuous jumps in the order parameter and normal fluctuations, while second order transitions are associated with no jumps and anomalous fluctuations. Outside this paradigm are systems exhibiting `mixed order transitions' displaying a mixture of these characteristics. When the jump is maximal and the fluctuations range over the entire range of allowed values, the behavior has been coined an `extreme Thouless effect'. Here, we report findings of such a phenomenon, in the context of dynamic, social networks. Defined by minimal rules of evolution, it describes a population of extreme introverts and extroverts, who prefer to have contacts with, respectively, no one or everyone. From the dynamics, we derive an exact distribution of microstates in the stationary state. With only two control parameters, $N_{I,E}$ (the number of each subgroup), we study collective variables of interest, e.g., $X$, the total number of $I$-$E $ links and the degree distributions. Using simulations and mean-field theory, we provide evidence that this system displays an extreme Thouless effect. Specifically, the fraction $X/\left( N_{I}N_{E}\right) $ jumps from $0$ to $1$ (in the thermodynamic limit) when $N_{I}$ crosses $N_{E}$, while all values appear with equal probability at $N_{I}=N_{E}$.