Description and simulation of dynamic mobility networks

Abstract

International audienceDuring the last decade, the study of large scale complex networks has attracted a substantial amount of attention and works from several domains: sociology, biology, computer science, epidemiology. Most of such complex networks are inherently dynamic, with new vertices and links appearing while some old ones disappear. Until recently, the dynamics of these networks was less studied and there is a strong need for dynamic network models in order to sustain protocol performance evaluations and fundamental analyzes in all the research domains listed above. We propose in this paper a novel framework for the study of dynamic mobility networks. We address the characterization of dynamics by proposing an in-depth description and analysis of two real-world data sets. We show in particular that links creation and deletion processes are independent of other graph properties and that such networks exhibit a large number of possible configurations, from sparse to dense. From those observations, we propose simple yet very accurate models that allow generate random mobility graphs with similar temporal behavior as the one observed in experimental dat

Dynamical and bursty interactions in social networks

“…This last quantity is highly relevant in the context of causal processes, as it gives the time scale on which an agent can propagate an information or a disease after receiving it. All these distributions are broad, similarly to empirical observations [11,25].…”

“…It is therefore necessary to introduce this fundamental aspect on human activity in models of social interactions, possibly reconstructing then social networks by aggregating the network of contacts over a certain period [26][27][28]. The modeling literature in this area being still in its infancy [25,[29][30][31][32], it is important to develop simple, generic, easily implementable models of dynamical networks which reproduce the empirical facts observed in contact duration and inter-contact intervals.In this Letter, we take a step in this direction, focusing on short timescales such as the ones involved when people interact in social gatherings (e.g., scientific conferences). We define a simple agent-based model for rapidly evolving sparse dynamical networks, aimed at describing the dynamics of human social interactions in the context of small discussion groups.…”

“…The duration of contacts between individuals or groups of individuals display indeed broad distributions, as well as the time intervals between successive contacts [6,11, 12,25]. Such heterogeneous behaviors have strong consequences on dynamical processes [4,26], and should therefore be correctly taken into account.…”

Social Dynamics in Conferences: Analyses of Data from the Live Social Semantics Application

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Providing Enhanced Social Interaction Services for Industry Exhibitors at Large Medical Conferences

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