Wavelets on Graphs: a Tool for Multiscale Community Mining in Graphs

Thursday, April 11, 2013
Speaker(s) : Nicolas TREMBLAY (ENS Lyon)

For data represented by networks, the community structure of the underlying graph is of great interest. A classical clustering problem is to uncover the overall “best” partition of nodes in communities. We work on a more elaborate description in which community structures are identified at different scales. To this end, we take advantage of the local and scale-dependent information encoded in graph wavelets. We classify nodes according to their wavelets or scaling functions, using, for instance, a scale-dependent modularity function. I will give an introduction on spectral graph wavelets and scaling functions, and talk about our recent advances. I will show results obtained on a graph benchmark having hierarchical structure and on real social networks.
This is joint work with my supervisor Pierre Borgnat.

More details here …
emilie.coupechoux (at)