Thomas Ricatte

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The aim of this paper is to propose methods for learning from interactions between groups in networks. For this, we introduce hypernode graphs as a formal tool able to model group interactions. A hypernode graph is a set of weighted binary relations between disjoint sets of nodes. We define Laplacians and kernels for hypernode graphs. And we propose(More)
This paper studies the problem of learning from a set of input graphs, each of them representing a different relation over the same set of nodes. Our goal is to merge those input graphs by embedding them into an Euclidean space related to the commute time distance in the original graphs. This is done with the help of a small number of labeled nodes. Our(More)
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