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We consider the problem of sampling k-bandlimited graph signals, ie, linearcombinations of the first k graph Fourier modes. We know that a set of k nodesembedding all k-bandlimited signals always exists, thereby enabling theirperfect reconstruction after sampling. Unfortunately, to exhibit such a set,one needs to partially diagonalize the graph Laplacian, which becomesprohibitive at large scale. We propose a novel strategy based on determinantalpoint processes that side-steps partial diagonalisation and enablesreconstruction with only O(k) samples. While doing so, we exhibit a new generalalgorithm to sample determinantal process, faster than the state-of-the-artalgorithm by an order k.

\'Echantillonnage de signaux sur graphes via des processus d\'eterminantaux