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Hypernode Graphs for Spectral Learning on Binary Relations over Sets

Thomas Ricatte 1 Rémi Gilleron 1, 2 Marc Tommasi 1, 2
1 MAGNET - Machine Learning in Information Networks
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe
Abstract : 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 ne-gative weight that represents the node contribution to the relation. Hypernode graphs model binary relations between sets of individuals while allowing to reason at the level of individuals. We present a spectral theory for hypernode graphs that allows us to introduce an unnormalized Laplacian and a smoothness semi-norm. In this framework, we are able to extend existing spec-tral graph learning algorithms to the case of hypernode graphs. We show that hypernode graphs are a proper extension of graphs from the expressive power point of view and from the spectral analysis point of view. Therefore hypernode graphs allow to model higher or-der relations while it has been shown in [1] that it is not the case for (classical) hypergraphs. In order to prove the capabilities of the model, we represent mul-tiple players games with hypernode graphs and intro-duce a novel method to infer skill ratings from the game outcomes. We show that spectral learning algorithms over hypernode graphs obtain competitive results with skill ratings specialized algorithms such as Elo duelling and TrueSkill.
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Thomas Ricatte, Rémi Gilleron, Marc Tommasi. Hypernode Graphs for Spectral Learning on Binary Relations over Sets. Conférence Francophone sur l'Apprentissage Automatique (Cap 2014), Jul 2014, Saint-Etienne, France. ⟨10.1007/978-3-662-44851-9_42⟩. ⟨hal-01104618⟩



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