On kernels for graphs.
Résumé : There is a growing need for adapting data analysis and machine learning methods to graphs, arising in different fields such as chemistry, biology, social science or image processing. Defining a graph kernel makes it possible to apply a whole spectrum of machine learning algorithms to graphs. These kernels have to respect the structure and node/edge labels of graphs and, importantly, they have to be efficient to compute in order to be applicable to large graphs. This talk will give an overview of different graph comparison methods and present a family of kernels for large graphs with discrete node labels. Particular instances of this family scale only linearly in the number of edges of the graphs, and outperform state-of-the-art graph kernels on several graph classification benchmark data sets in terms of accuracy and runtime. At the end of the talk we will discuss the next challenges in graph comparison.
Cet exposé se tiendra en salle C20-13, 20ème étage, Université Paris 1, Centre Pierre Mendès-France, 90 rue de Tolbiac, 75013 Paris (métro : Olympiades).