Pierre Latouche

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With the flourishing development of high-dimensional data, sparse versions of principal component analysis (PCA) have imposed themselves as simple, yet powerful ways of selecting relevant features in an unsupervised manner. However, when several sparse principal components are computed, the interpretation of the selected variables may be difficult since(More)
We develop a model in which interactions between nodes of a dynamic network are counted by non-homogeneous Poisson processes. In a block modelling perspective, nodes belong to hidden clusters (whose number is unknown) and the intensity functions of the counting processes only depend on the clusters of nodes. In order to make inference tractable, we move to(More)
This paper proposes an approach for comparing interlocked board networks over time to test for statistically significant change. In addition to contributing to the conversation about whether the Mizruchi hypothesis (that a disintegration of power is occurring within the corporate elite) holds or not, we propose novel methods to handle a longitudinal(More)
The stochastic block model (SBM) [1] describes interactions between nodes of a network following a probabilistic approach. Nodes belong to hidden clusters and the probabilities of interactions only depend on these clusters. Interactions of time varying intensity are not taken into account. By partitioning the whole time horizon, in which interactions are(More)