1. Classification and regression over graphs. Overview: positive definite graph kernels based on walk, subtrees etc.., as well as other non p.d. similarity functions (eg from graph matching) that can be used to compare graphs and do classification/regression with kernel methods. Applications: QSAR in chemistry, image classification 2. Detecting patterns in the context of regression or classification with a graph as prior knowledge over the features.  Overview: in a classical regression/classification problem over high-dimensional vectors. Control the complexity, by using priors that can be derived from the graph over the vectors, and how they can be used as penalty functions for classification and regression. This will cover diffusion kernels and other kernels over graphs, fused lasso, structured group lasso. Application in bioinformatics.