Today's technology is producing data at an ever increasing rate, and part if this data can be modeled as graphs: a set of nodes (or “vertices”), and links (or “edges”) between nodes. Here are a few examples:
|Genome||genes||activations or inhibitions|
In 1967, Milgram observed that almost anyone can be reached through a short chain of social acquaintances, despite the fact that the world population is huge. This is the small world effect. Not only this social network, but practically all real networks have this property. Another common point of those networks is the existence of a few hubs with a disproportionately large number of connections (think of London's airports) and many small nodes. Thanks to a few more common properties, it is thus relevant to design common methods to apply to all those networks: this is done mainly by mathematicians, physicists, computer scientists, while the study of specific networks are done by specialists like biologists, linguists, geographers, etc.
Complex network theory can be structured in five branches:
Example applications include:
I work on networks arising in various (quite different) domains, specifically on communities, structure, dynamics. I have an interest for large datasets (for instance, I have worked on the Vélo'v / Vélib network, and on co-authorship networks.)
My publications list is here