Section: Overall Objectives

Overall Objectives

The main goal of the D-NET team is to lay solid foundations to the characterization of dynamic networks, and to the field of dynamic processes occurring on large scale interaction networks. In order to develop tools of practical relevance in real-world settings, we propose to ground our methodological studies on real data sets obtained through large scale in situ experiments.

Let us consider the example of health science and public health policy. The spread of infectious diseases remains an urgent public health issue. All spreading models validity crucially depend on our ability to understand and describe the interactions among individuals in the population. Building this knowledge requires the availability of individuals interaction records. Only recently it has become possible to study large scale interaction networks, such as collaboration networks, e-mail or phone call networks, sexual contacts networks, etc. This has prompted many research efforts in complex network science, mainly in two directions. First, attention has been paid to the network structure, considered as static graphs. Second, a large amount of work has focused on the study of spreading models in complex networks, which has highlighted the role of the network topology on the dynamics of the spreading. However, the dynamics of the networks, i.e., topology changes, and in the networks, e.g., spreading processes, are still generally studied separately. There is therefore an important need developing tools and methods for the joint analysis of both dynamics.

The D-NET project emphasizes the cross fertilization between these two research lines which should definitively lead to considerable advances. The D-NET project has the following fundamental goals:

1. To develop distributed measurement architectures based on sensor networks in order to capture physical phenomena in space and time;

2. To develop the study of dynamic interaction networks, through the design of specific tools targeted at characterizing and modeling their dynamic properties.

3. To study dynamic processes occurring in dynamic networks, such as spreading processes, taking into account both the dynamics of and in the network structure.

4. To apply these theoretical tools to large scale experimental data sets.

5. to set up and foster multidisciplinary collaborations in order to study these interaction networks in their original context.

Most activity on complex networks has up to now focused on static networks, the characterization of their structure, and the understanding of how their structure influences dynamic processes such as spreading phenomenon. The important step that the D-NET project wants to undertake is to consider that the networks themselves are dynamic entities. Their topologies evolve and adapt in time, possibly driven by or in interaction with the dynamic process unfolding on top of it. Measuring this dynamic is now affordable mainly thanks to the deployment of sensor networks that can be deployed close to the physical interacting objects. As an example, in the MOSAR context, sensors measure contacts between individuals. The resulting data set is an opportunity to develop analysis methods and tools directly connected to real-world situations and adapted to the specific context from where the data set is issued. Still in the MOSAR context, a better understanding of dynamic processes on dynamic interaction networks should help the development of appropriate response measures and protocols to the spreading of AMRB.

The D-NET project therefore addresses both very fundamental and very applied aspects that are tightly linked. On one hand, to develop knowledge in the networking field, in order to provide a better understanding of dynamic graphs. This fundamental work is grounded on real world large scale dynamic networks. On the other hand, to help develop a better understanding of the physical objects and networks that are studied. This point requires the joint study of both dynamics of the network and in the network, and requires a tied collaboration with the research disciplines where the objects come from.

The impact of the research developed in D-NET goes beyond the context of disease spreading studied within the MOSAR context, thanks to the inherent interdisciplinary of the complex networks research field. Dynamic processes on dynamic networks are indeed present in numerous fields, including rumor spreading in social networks, opinion formation, fashion phenomena, the innovation diffusion in a population, etc. The spread of computer viruses may take place through email networks or bluetooth connections, which are both dynamical. The development of efficient algorithms for information spreading in wireless/P2P/DTN networks should also be improved by the understanding of the dynamics of these networks and their temporal properties. The study of all these processes should benefit from the tools developed in this project. It represents an important opportunity to study real-world dynamic processes occurring on interaction networks whose dynamics can be measured.