Dynamic Networks : Temporal and Structural Capture Approach
Dynamic Networks : Temporal and Structural Capture Approach

The main goal of the DANTE team is to lay solid foundations to the characterisation of dynamic networks, and to the field of dynamic processes occurring on large scale dynamic networks. In order to develop tools of practical relevance in real-world settings, we propose to ground our methodological studies on real data sets. Indeed, large datasets describing such networks are nowadays more "accessible" due to the emergence of online activities and new techniques of data collection. These advantages provide us an un-precedent avalanche of large data sets, recording the digital footprints of millions of entities (e.g. individuals, computers, documents, stocks, memes etc.) and their temporal interactions. 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 DANTE project emphasises the cross fertilisation between these two research lines which should definitively lead to considerable advances. Our main challenge is to propose generic methodologies and concepts to develop relevant formal tools to model, analyse the dynamics and evolution of such networks, that is, to formalise the dynamic properties of both structural and temporal interactions of network entities/relations:

  • Ask application domains relevant questions
  • Access and collect data with adapted and efficient tools
  • Model the dynamics of networks by analysing their structural and temporal properties jointly, inventing original approaches combining graph theory with signal processing
  • Interpret the results, make the knowledge robust and useful in order to be able to control, optimise and (re)-act on the network structure itself
Centre(s) inria
In partnership with
Université Claude Bernard (Lyon 1),Ecole normale supérieure de Lyon


Team leader

Gerard Sophie

Team assistant