Dynamic and Topological Interplay in Adaptive Networks

Blasius, Bernd; Groß, Thilo

doi:10.1002/9783527628001.ch3

Cited by 3 publications

(4 citation statements)

References 82 publications

(114 reference statements)

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“…For models on adaptive networks, there is a subtle point concerning the time scales that govern the evolution of the network itself and of the model (in our case, the naming game). In principle, these processes might have two independent time scales, which in some cases might even be well separated [20,4]. In our model, however, the evolution of weights is strongly coupled with agreeing dynamics of the naming game.…”

Section: Remarks and Conclusionmentioning

confidence: 99%

Naming Game on Adaptive Weighted Networks

Lipowska

Lipowski

2012

Artificial Life

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We examine a naming game on an adaptive weighted network. A weight of connection for a given pair of agents depends on their communication success rate and determines the probability with which the agents communicate. In some cases, depending on the parameters of the model, the preference toward successfully communicating agents is essentially negligible and the model behaves similarly to the naming game on a complete graph. In particular, it quickly reaches a single-language state, albeit some details of the dynamics are different from the complete-graph version. In some other cases, the preference toward successfully communicating agents becomes much more important and the model gets trapped in a multi-language regime. In this case gradual coarsening and extinction of languages lead to the emergence of a dominant language, albeit with some other languages still present. A comparison of distribution of languages in our model and in the human population is discussed.

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Section: Remarks and Conclusionmentioning

confidence: 99%

Naming Game on Adaptive Weighted Networks

Lipowska

Lipowski

2012

Artificial Life

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“…Below we discuss three of the 'hallmarks' of adaptive networks taken from Blasius and Gross [56], each of which has implications for adaptive networks in Web Science.…”

Section: Discussionmentioning

confidence: 99%

“…the emergence of patterns in a distributed system without any central control [60]. Blasius and Gross discuss examples where the adaptive feedback inherent in an adaptive network "enables the agents that form the network to robustly organize into a state with special topological or dynamical properties" such as self-organized criticality and power-law distributions [56].…”

Section: Discussionmentioning

confidence: 99%

The web as an adaptive network

McCabe

Watson

Prichard

et al. 2011

Proceedings of the 3rd International Web Science Conference

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Much is known about the complex network structure of the Web, and about behavioral dynamics on the Web. A number of studies address how behaviors on the Web are affected by different network topologies, whilst others address how the behavior of users on the Web alters network topology. These represent complementary directions of influence, but they are generally not combined within any one study. In network science, the study of the coupled interaction between topology and behavior, or statetopology coevolution, is known as 'adaptive networks', and is a rapidly developing area of research. In this paper, we review the case for considering the Web as an adaptive network and several examples of state-topology coevolution on the Web. We also review some abstract results from recent literature in adaptive networks and discuss their implications for Web Science. We conclude that adaptive networks provide a formal framework for characterizing processes acting 'on' and 'of' the Web, and offers potential for identifying general organizing principles that seem otherwise illusive in Web Science.

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“…The dynamics 'on' a network describe the state transitions of the network's nodes, while the dynamics 'of' a network describe topological changes. Research on so-called coevolutionary networks recognises that these processes are inherently reflexive, with network state influencing topological change (as when edges are formed between similar nodes), and topology constraining state change (as when neighbours exchange information) (Blasius and Gross, 2009;Gross and Blasius, 2008;Gross and Sayama, 2009). Coevolutionary networks have been the subject of recent study in the context of the epidemic spread of diseases (Newman, 2002;Zhong et al, 2010;Funk and Jansen, 2010;Van Segbroeck et al, 2010), cascading network behaviour (Watts, 2002), opinion dynamics (Kozma and Barrat, 2008;Demirel et al, 2011), diffusion of innovations / information (Onnela and Reed-Tsochas, 2009;Ke and Yi, 2008), evolution of social groups (Palla et al, 2007), the growth of social networks (Sun and Wang, 2008), co-operation (Pacheco et al, 2006;Van Segbroeck et al, 2009), community formation (Bryden et al, 2010), synchronisation (Zhu et al, 2010) and global adaptation (Watson et al, in press).…”

Section: Timescales On Networkmentioning

confidence: 99%

On the interaction of adaptive timescales on networks

2011

ECAL 2011: The 11th European Conference on Artificial Life

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The dynamics of real-world systems often involve multiple processes that influence system state. The timescales that these processes operate on may be separated by orders of magnitude or may coincide closely. Where timescales are not separable, the way that they relate to each other will be important for understanding system dynamics. In this paper, we present a short overview of how modellers have dealt with multiple timescales and introduce a definition to formalise conditions under which timescales are separable.We investigate timescale separation in a simple model, consisting of a network of nodes on which two processes act. The first process updates the values taken by the network's nodes, tending to move a node's value towards that of its neighbours. The second process influences the topology of the network, by rewiring edges such that they tend to more often lie between similar individuals. We show that the behaviour of the system when timescales are separated is very different from the case where they are mixed. When the timescales of the two processes are mixed, the ratio of the rates of the two processes determines the systems equilibrium state. We go on to explore the impact of heterogeneity in the system's timescales, i.e., where some nodes may update their value and/or neighbourhood faster than others, demonstrating that it can have a significant impact on the equilibrium behaviour of the model.

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Dynamic and Topological Interplay in Adaptive Networks

Cited by 3 publications

References 82 publications

Naming Game on Adaptive Weighted Networks

Naming Game on Adaptive Weighted Networks

The web as an adaptive network

On the interaction of adaptive timescales on networks

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