Agreement dynamics on interaction networks with diverse topologies

Barrat, Alain; Baronchelli, Andrea; Dall’Asta, Luca; Loreto, Vittorio

doi:10.1063/1.2734403

Cited by 27 publications

(24 citation statements)

References 33 publications

(30 reference statements)

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“…It is interesting to 036110-3 . This relationship is in agreement with what has been observed in the literature for small-world, scale-free, and random networks [6,7] where N max w scales as O(N ).…”

Section: Analysis Of N Max Wsupporting

confidence: 88%

“…Most of these social phenomena can be modeled and analyzed in a time-varying framework. Almost all previous work is limited to the analysis of the naming game dynamics on static networks [1][2][3][4][5][6][7][8][9]. Therefore, in this paper we focus on the competing opinion formation over time-varying real-world social networks.…”

Section: Introductionmentioning

confidence: 97%

“…Apart from this mean-field case, the model has also been studied on regular lattices [2,3]; small world networks [3][4][5][6]; random geometric graphs [3,18,19]; and static [7][8][9], dynamic, adaptive [20], and empirical [21] complex networks.…”

Section: Introductionmentioning

confidence: 99%

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Opinion formation in time-varying social networks: The case of the naming game

2012

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We study the dynamics of the naming game as an opinion formation model on time-varying social networks. This agent-based model captures the essential features of the agreement dynamics by means of a memory-based negotiation process. Our study focuses on the impact of time-varying properties of the social network of the agents on the naming game dynamics. In particular, we perform a computational exploration of this model using simulations on top of real networks. We investigate the outcomes of the dynamics on two different types of time-varying data: (1) the networks vary on a day-to-day basis and (2) the networks vary within very short intervals of time (20 sec). In the first case, we find that networks with strong community structure hinder the system from reaching global agreement; the evolution of the naming game in these networks maintains clusters of coexisting opinions indefinitely leading to metastability. In the second case, we investigate the evolution of the naming game in perfect synchronization with the time evolution of the underlying social network shedding new light on the traditional emergent properties of the game that differ largely from what has been reported in the existing literature.

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Section: Analysis Of N Max Wsupporting

confidence: 88%

Section: Introductionmentioning

confidence: 97%

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Opinion formation in time-varying social networks: The case of the naming game

2012

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“…There have been several studies that have considered the naming game model (with two opinions) for opinion dynamics; for example, see Refs. [15][16][17][18]. In this work, we focus on the situation where there are a large number of opinions in the presence of zealots.…”

Section: Introductionmentioning

confidence: 99%

Effect of zealotry in high-dimensional opinion dynamics models

et al. 2015

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Most of the work on opinion dynamics models focuses on the case of two or three opinion types. We consider the case of an arbitrary number of opinions in the mean field case of the naming game model in which it is assumed the population is infinite and all individuals are neighbors. A particular challenge of the naming game model is that the number of variables, which corresponds to the number of possible sets of opinions, grows exponentially with the number of possible opinions. We present a method for generating mean field dynamical equations for the general case of k opinions. We calculate the steady states in two important special cases in arbitrarily high dimension: the case in which there exist zealots of only one type, and the case in which there are an equal number of zealots for each opinion. We show that in these special cases a phase transition occurs at critical values p(c) of the parameter p describing the fraction of zealots. In the former case, the critical value determines the threshold value beyond which it is not possible for the opinion with no zealots to be held by more nodes than the opinion with zealots, and this point remains fixed regardless of dimension. In the latter case, the critical point p(c) is the threshold value beyond which a stalemate between all k opinions is guaranteed, and we show that it decays precisely as a lognormal curve in k.

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“…Barrat et al [22] address the emergence of consensus in linguistic conventions, Bogacz et al [23] consider condensation and far from equilibrium dynamics on networks, and Freire et al [24] analyze synchronization and complex spatiotemporal patterns in networks of cellular automata.…”

Section: This Focus Issuementioning

confidence: 99%

Introduction: Optimization in networks

Motter

Toroczkai

2007

Chaos: An Interdisciplinary Journal of Nonlinear Science

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The recent surge in the network modeling of complex systems has set the stage for a new era in the study of fundamental and applied aspects of optimization in collective behavior. This Focus Issue presents an extended view of the state of the art in this field and includes articles from a large variety of domains where optimization manifests itself, including physical, biological, social, and technological networked systems.PACS numbers: 89.75.k, 87.18.Sn, 87.16.Yc One of the broadest areas of research, optimization has a very long history. It comprises the variational principles in physics and engineering, the survival-of-the-fittest principles that pervade biology and economics, the founding hypotheses of numerous computer algorithms, and the frameworks for addressing the improvement of efficiency in various contexts. Whether a fact or a goal, a natural process or a man-made system, the apparently ubiquitous striving for optimization generates continuing appeal among researchers. But what is new about optimization in networked systems?Real-world systems do not operate isolated from each other. While a neuron can be studied in a laboratory setting, it has not evolved to work independently of the activity of other neurons nor has the brain evolved to work independently from the organism. In a hierarchy of scales, many systems are formed by the interconnection of subsystems that may have different (or even opposing) optimization goals than the global system which they are part of. Expectedly, the structure of these interconnections will influence the global performance and hence complex network research [1, 2] is a key ingredient for studying optimal system behavior (see Fig. 1). Not surprisingly, various structural and dynamical network properties have been explicitly related to the optimization of specific functions (see, e.g., Refs. [3, 4, 5] for early works and Refs. [6,7,8,9, 10] for recent reviews). In this context, there are entire classes of problems, ranging from epidemic spreading [11] to the control of cascading failures [12], which are naturally defined as extremization problems. Others, such as the unexpected robustness observed in some systems, involve no a priory optimization * E-mail address:motter@northwestern.edu † E-mail address:toro@nd.educonditions and yet reveal enhanced properties shaped by the evolution of the system.Optimal behavior is most often connected to a function that the system performs. In numerous cases the function is multi-variate or multi-faceted. For example, the power grid has as its main role the transport of electric energy while minimizing generation and distribution costs and maximizing at the same time reliability and quality of service. 'How network structure influences the global performance of such systems' is probably the question that is posed most frequently in network research. Conversely, the notion of network acts as a unifying theme in systems optimization. Indeed, with the increasing abundance of empirical and theoretical results, numerous optimization FI...

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Agreement dynamics on interaction networks with diverse topologies

Cited by 27 publications

References 33 publications

Opinion formation in time-varying social networks: The case of the naming game

Opinion formation in time-varying social networks: The case of the naming game

Effect of zealotry in high-dimensional opinion dynamics models

Introduction: Optimization in networks

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