Section: Research Program

Theory and Structure of dynamic Networks

Participants : Christophe Crespelle, Éric Fleury, Anthony Busson, Márton Karsai, Jean-Philippe Magué, Éric Guichard, Jean-Pierre Chevrot, Tommaso Venturini.

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Through the systematic analysis and characterization of static network representations of many different systems, researchers of several disciplines have unveiled complex topologies and heterogeneous structures, with connectivity patterns statistically characterized by heavy-tails and large fluctuations, scale-free properties and non trivial correlations such as high clustering and hierarchical ordering  [73]. A large amount of work has been devoted to the development of new tools for statistical characterisation and modelling of networks, in order to identify their most relevant properties, and to understand which growth mechanisms could lead to these properties. Most of those contributions have focused on static graphs or on dynamic process (e.g. diffusion) occurring on static graphs. This has called forth a major effort in developing the methodology to characterize the topology and temporal behaviour of complex networks  [73], [63], [80], [69], to describe the observed structural and temporal heterogeneities  [56], [63], [57], to detect and measure emerging community structures  [60], [77], [78], to see how the functionality of networks determines their evolving structure  [68], and to determine what kinds of correlations play a role in their dynamics  [64], [67], [72].

The challenge is now to extend this kind of statistical characterization to dynamical graphs. In other words, links in dynamic networks are temporal events, called contacts, which can be either punctual or last for some period of time. Because of the complexity of this analysis, the temporal dimension of the network is often ignored or only roughly considered. Therefore, fully taking into account the dynamics of the links into a network is a crucial and highly challenging issue.

Another powerful approach to model time-varying graphs is via activity driven network models. In this case, the only assumption relates to the distribution of activity rates of interacting entities. The activity rate is realistically broadly distributed and refers to the probability that an entity becomes active and creates a connection with another entity within a unit time step  [75]. Even the generic model is already capable to recover some realistic features of the emerging graph, its main advantage is to provide a general framework to study various types of correlations present in real temporal networks. By synthesising such correlations (e.g. memory effects, preferential attachment, triangular closing mechanisms, ...) from the real data, we are able to extend the general mechanism and build a temporal network model, which shows certain realistic feature in a controlled way. This can be used to study the effect of selected correlations on the evolution of the emerging structure  [66] and its co-evolution with ongoing processes like spreading phenomena, synchronisation, evolution of consensus, random walk etc.  [66], [74]. This approach allows also to develop control and immunisation strategies by fully considering the temporal nature of the backgrounding network.