Mutual selection in time-varying networks

Hoppe, K.; Rodgers, G. J.

doi:10.1103/physreve.88.042804

Cited by 27 publications

(28 citation statements)

References 25 publications

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“…In this formulation inactive nodes can receive connections. Different rules can be easily implemented to model different scenarios 50 . Without loss of generality we fix the parameters η = 1, , and Δ t = 1.…”

Section: Resultsmentioning

confidence: 99%

Time varying networks and the weakness of strong ties

Karsai

Perra

Vespignani

2014

Sci Rep

237

300

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In most social and information systems the activity of agents generates rapidly evolving time-varying networks. The temporal variation in networks' connectivity patterns and the ongoing dynamic processes are usually coupled in ways that still challenge our mathematical or computational modelling. Here we analyse a mobile call dataset and find a simple statistical law that characterize the temporal evolution of users' egocentric networks. We encode this observation in a reinforcement process defining a time-varying network model that exhibits the emergence of strong and weak ties. We study the effect of time-varying and heterogeneous interactions on the classic rumour spreading model in both synthetic, and real-world networks. We observe that strong ties severely inhibit information diffusion by confining the spreading process among agents with recurrent communication patterns. This provides the counterintuitive evidence that strong ties may have a negative role in the spreading of information across networks.

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Section: Resultsmentioning

confidence: 99%

Time varying networks and the weakness of strong ties

Karsai

Perra

Vespignani

2014

Sci Rep

237

300

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“…In this model random nodes copy the opinion of random neighbors. Although very popular in static networks, we only know two works [111,66] studying it on temporal networks. In this study, the temporal network is a result from a mutual selection process.…”

Section: Opinion and Information Spreadingmentioning

confidence: 99%

Modern temporal network theory: a colloquium

2015

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Abstract. The power of any kind of network approach lies in the ability to simplify a complex system so that one can better understand its function as a whole. Sometimes it is beneficial, however, to include more information than in a simple graph of only nodes and links. Adding information about times of interactions can make predictions and mechanistic understanding more accurate. The drawback, however, is that there are not so many methods available, partly because temporal networks is a relatively young field, partly because it more difficult to develop such methods compared to for static networks. In this colloquium, we review the methods to analyze and model temporal networks and processes taking place on them, focusing mainly on the last three years. This includes the spreading of infectious disease, opinions, rumors, in social networks; information packets in computer networks; various types of signaling in biology, and more. We also discuss future directions.

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“…In many social, biological, and physical systems the interactions among the elements of the system are rapidly changing [26,27]. In recent years, there have been extensive research activities on time-varying networks, or called temporal networks, which evolve on a time scale comparable to the timescale of the propagation process [28,29]. The time-varying structure of link activations affects the network dynamics, from disease contagion to information diffusion [30,31].…”

Section: Introductionmentioning

confidence: 99%

An evolutionary vaccination game in the modified activity driven network by considering the closeness

Han

Sun

2016

Physica A: Statistical Mechanics and its Applications

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h i g h l i g h t s• The evolutionary vaccination game is considered in a modified activity driven network. • A closeness parameter p which is used to describe the connection between individuals is presented. • The closeness p may have an active role in weakening both the spreading of epidemic and the vaccination.• Infected frequency increases with the increase of the individuals' activity.• Results indicate some better hope (such as reducing the connection) may lead to backfire. a b s t r a c tIn this paper, we explore an evolutionary vaccination game in the modified activity driven network by considering the closeness. We set a closeness parameter p which is used to describe the way of connection between two individuals. The simulation results show that the closeness p may have an active role in weakening both the spreading of epidemic and the vaccination. Besides, when vaccination is not allowed, the final recovered density increases with the value of the ratio of the infection rate to the recovery rate λ/µ. However, when vaccination is allowed the final density of recovered individual first increases and then decreases with the value of λ/µ. Two variables are designed to identify the relation between the individuals' activities and their states. The results draw that both recovered and vaccinated frequency increase with the increase of the individuals' activities. Meanwhile, the immune fee has less impact on the individuals' vaccination than the closeness.While the λ/µ is in a certain range, with the increase of the value of λ/µ, the recovered frequency of the whole crowds reduces. Our results, therefore, reveal the fact that the best of intentions may lead to backfire.

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Mutual selection in time-varying networks

Cited by 27 publications

References 25 publications

Time varying networks and the weakness of strong ties

Time varying networks and the weakness of strong ties

Modern temporal network theory: a colloquium

An evolutionary vaccination game in the modified activity driven network by considering the closeness

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