Complex Networks Analysis
This work focuses on the analysis of social networks or interaction networks in order to detect communities or behaviours. Temporal evolution of interactions and communities is a major concern.
The approaches used are:
- detection of temporal communities in graphs that may have attributes on nodes and/or edges
- graphlet identification (small induced subgraphs)
- machine learning tools for classification and clustering
- 2D visualization tools for community evolution
More broadly, this theme also focuses mechanisms of information propagation within social networks. The influence of network topology and the nature of the messages carried on the propagation of information is studied.
We have many fields of application for this work: crowdfunding study, echo chambers detection in community platforms, influence of interactions on the quality of student learning but also the study of the dynamics of biological networks.