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

Thursday, April 11, 2013
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) nulllip6.fr