Complex networks appear in many contexts: sociology, with friendship networks, collaboration networks, computer networks, with the Internet, the World Wide Web, blog networks, P2P networks, biology, with food webs, genetic regulatory networks, metabolic networks, epidemiology, energy and transportation with road networks, power grids, railways, airline routes, but also in economics, linguistics and many others.

The CODDDE project aims at studying critical research issues in the field of real-world complex networks study:

• How do these networks evolve over time?
• How does information spread on these networks?
• How can we detect and predict unexpected changes in their structure?

In order to answer these questions, an essential feature of complex networks will be exploited: the existence of a community structure among nodes of these networks. Complex networks are indeed composed of internally densely connected groups that have few interactions with one another.

The CODDE project will therefore propose new community detection algorithms to reflect complex networks evolution, in particular with regards to diffusion phenomena and anomaly detection. These algorithms and methodology will be applied and validated on a real-world online social network consisting of more than 10 000 blogs and French media collected since 2009 on a daily basis (the dataset comprises all published articles and the links between these articles) correlated with a twitter dataset.