Fragmentation transitions in multi-state voter models

Abstract

Adaptive models of opinion formation among humans can display a fragmentation transition, where a social network breaks into disconnected components. Here, we investigate this transition in a class of models with arbitrary number of opinions. In contrast to previous work we do not assume that opinions are equidistant or arranged on a one-dimensional conceptual axis. Our investigation reveals detailed analytical results on fragmentations in a three-opinion model, which are confirmed by agent-based simulations. Furthermore, we show that in certain models the number of opinions can be reduced without affecting the fragmentation points.

11 pages, 8 figures

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• Noise in Coevolving Networks
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• A multi-opinion evolving voter model with infinitely many phase transitions
• Exploring the adaptive voter model dynamics with a mathematical triple jump
• Transitivity reinforcement in the coevolving voter model
• Role of social environment and social clustering in spread of opinions in co-evolving networks
• Adaptive network models of collective decision making in swarming systems
• Spontaneous symmetry breaking of active phase in coevolving nonlinear voter model
• Emergence and persistence of communities in coevolutionary networks
• Finite-size effects on the convergence time in continuous-opinion dynamics
• Dynamics of Influence on Hierarchical Structures
• Memory-based reduced modelling and data-based estimation of opinion spreading
• Moment-Closure Approximations for Discrete Adaptive Networks
• Local Symmetry and Global Structure in Adaptive Voter Models
• Co-evolution of networks and quantum dynamics: a generalization of preferential attachment
• Preliminary Study on Bit-String Modelling of Opinion Formation in Complex Networks