Thomas Ricatte

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We introduce hypernode graphs as weighted binary relations between sets of nodes: a hypernode is a set of nodes, a hyperedge is a pair of hypernodes, and each node in a hypernode of a hyperedge is given a non negative weight that represents the node contribution to the relation. Hypernode graphs model binary relations between sets of individuals while(More)
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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