Claire Donnat

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Nodes residing in different parts of a graph can have similar structural roles within their local network topology. The identification of such roles provides key insight into the organization of networks and can also be used to inform machine learning on graphs. However, learning structural representations of nodes is a challenging unsupervised-learning(More)
Given data that lies in a union of low-dimensional subspaces, the problem of subspace clustering aims to learn— in an unsupervised manner—the membership of the data to their respective subspaces. State-of-the-art subspace clustering methods typically adopt a two-step procedure. In the first step, an affinity measure among data points is constructed, usually(More)
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