Campus Jussieu, Salle 26-00/124 à 11h00

**Abstract**

Real-world graphs (a.k.a. complex networks) are ubiquitous: the web, Facebook, Twitter, brain networks, protein interaction networks, etc. Studying these graphs and solving computational problems on them (say maximum clique, partitioning or dense subgraph) has applications in many fields.

I will first show that the structure of some real-world graphs is special. In particular, they are not adversarial and some difficult problems (say NP-complete or NP-hard problems) can be solved on some huge real-world graphs (say 2G edges or more).

I will then present two works along the lines of "understanding and leveraging the structure of real-world graphs in order to build better algorithms": (i) Enumerating all k-cliques and (ii) Computing the density-friendly graph decomposition.

I will first show that the structure of some real-world graphs is special. In particular, they are not adversarial and some difficult problems (say NP-complete or NP-hard problems) can be solved on some huge real-world graphs (say 2G edges or more).

I will then present two works along the lines of "understanding and leveraging the structure of real-world graphs in order to build better algorithms": (i) Enumerating all k-cliques and (ii) Computing the density-friendly graph decomposition.