In this talk, we consider the problem of classifying
Multivariate Hawkes Processes (MHP) paths coming from several classes discriminated by the exogenous intensity vector and the adjacency matrix, which encodes the strength of the interactions. We consider the high-dimensional setting, meaning the dimension of the network may be large w.r.t. the number of observations. In this context, we propose a novel methodology with an initial interaction recovery step, by class, followed by a refitting step based on a suitable classification criterion. To recover the support of the adjacency matrix, a Lasso-type estimator is proposed, for which we establish rates of convergence. Then, leveraging the estimated support, we build a classification procedure based on the minimization of a L2-risk.