Adaptive coevolutionary networks: a review

Groß, Thilo; Blasius, Bernd

doi:10.1098/rsif.2007.1229

Cited by 887 publications

(812 citation statements)

References 78 publications

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“…The results rendered with our tolerance model also fall in line with a research direction started by Gross & Blasius (2008) where the authors show that there is a self-organization in all adaptive networks, including multi-agent opinion networks. Our real-world…”

Section: Discussionsupporting

confidence: 88%

Tolerance-based interaction: a new model targeting opinion formation and diffusion in social networks

Topîrceanu

Udrescu

Vlăduţiu

et al. 2016

PeerJ Computer Science

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One of the main motivations behind social network analysis is the quest for understanding opinion formation and diffusion. Previous models have limitations, as they typically assume opinion interaction mechanisms based on thresholds which are either fixed or evolve according to a random process that is external to the social agent. Indeed, our empirical analysis on large real-world datasets such as Twitter, Meme Tracker, and Yelp, uncovers previously unaccounted for dynamic phenomena at population-level, namely the existence of distinct opinion formation phases and social balancing. We also reveal that a phase transition from an erratic behavior to social balancing can be triggered by network topology and by the ratio of opinion sources. Consequently, in order to build a model that properly accounts for these phenomena, we propose a new (individual-level) opinion interaction model based on tolerance. As opposed to the existing opinion interaction models, the new tolerance model assumes that individual's inner willingness to accept new opinions evolves over time according to basic human traits. Finally, by employing discrete event simulation on diverse social network topologies, we validate our opinion interaction model and show that, although the network size and opinion source ratio are important, the phase transition to social balancing is mainly fostered by the democratic structure of the small-world topology.

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Section: Discussionsupporting

confidence: 88%

Tolerance-based interaction: a new model targeting opinion formation and diffusion in social networks

Topîrceanu

Udrescu

Vlăduţiu

et al. 2016

PeerJ Computer Science

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“…To study the social implications of deception, we use an agentbased model of opinion formation in coevolving social networks [49,51], in which a continuous state variable x i (t) representing the instantaneous opinion of agent i at time t about an issue or topic is bounded between 21 and þ1 (i.e. total disagreement and total agreement, respectively).…”

Section: Methodsmentioning

confidence: 99%

Effects of deception in social networks

et al. 2014

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Honesty plays a crucial role in any situation where organisms exchange information or resources. Dishonesty can thus be expected to have damaging effects on social coherence if agents cannot trust the information or goods they receive. However, a distinction is often drawn between prosocial lies ('white' lies) and antisocial lying (i.e. deception for personal gain), with the former being considered much less destructive than the latter. We use an agentbased model to show that antisocial lying causes social networks to become increasingly fragmented. Antisocial dishonesty thus places strong constraints on the size and cohesion of social communities, providing a major hurdle that organisms have to overcome (e.g. by evolving counter-deception strategies) in order to evolve large, socially cohesive communities. In contrast, white lies can prove to be beneficial in smoothing the flow of interactions and facilitating a larger, more integrated network. Our results demonstrate that these grouplevel effects can arise as emergent properties of interactions at the dyadic level. The balance between prosocial and antisocial lies may set constraints on the structure of social networks, and hence the shape of society as a whole.

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“…However, presumably, what other vertices vertex i is connected to could affect the times when i does something, and vice versa. This question comes close to the goal of adaptive network studies [48] that model the feedback from network structure (and how it affects dynamics on the network) to the success of the agents forming the network (and how they seek to change their position in it). If one could include when contacts happen along an edge into adaptive network models and thereby explain some observed temporal-topological correlations, this would be a breakthrough (no matter what the objective system is).…”

Section: Future Outlookmentioning

confidence: 99%

Temporal networks

2012

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A great variety of systems in nature, society and technology-from the web of sexual contacts to the Internet, from the nervous system to power grids-can be modeled as graphs of vertices coupled by edges. The network structure, describing how the graph is wired, helps us understand, predict and optimize the behavior of dynamical systems. In many cases, however, the edges are not continuously active. As an example, in networks of communication via email, text messages, or phone calls, edges represent sequences of instantaneous or practically instantaneous contacts. In some cases, edges are active for non-negligible periods of time: e.g., the proximity patterns of inpatients at hospitals can be represented by a graph where an edge between two individuals is on throughout the time they are at the same ward. Like network topology, the temporal structure of edge activations can affect dynamics of systems interacting through the network, from disease contagion on the network of patients to information diffusion over an e-mail network. In this review, we present the emergent field of temporal networks, and discuss methods for analyzing topological and temporal structure and models for elucidating their relation to the behavior of dynamical systems. In the light of traditional network theory, one can see this framework as moving the information of when things happen from the dynamical system on the network, to the network itself. Since fundamental properties, such as the transitivity of edges, do not necessarily hold in temporal networks, many of these methods need to be quite different from those for static networks. The study of temporal networks is very interdisciplinary in nature. Reflecting this, even the object of study has many names

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Adaptive coevolutionary networks: a review

Cited by 887 publications

References 78 publications

Tolerance-based interaction: a new model targeting opinion formation and diffusion in social networks

Tolerance-based interaction: a new model targeting opinion formation and diffusion in social networks

Effects of deception in social networks

Temporal networks

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