Title :

Hypernode Graphs for Spectral Learning on Binary Relations over Sets

Author(s) :

Ricatte, Thomas [Auteur]
Machine Learning in Information Networks [MAGNET]
Gilleron, Remi [Auteur]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Machine Learning in Information Networks [MAGNET]
Tommasi, Marc [Auteur]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Machine Learning in Information Networks [MAGNET]

Conference title :

ECML/PKDD - 7th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases

City :

Nancy

Country :

France

Start date of the conference :

2014-09-15

Journal title :

Machine Learning and Knowledge Discovery in Databases

Publication date :

2014

English keyword(s) :

Graphs
Hypergraphs
Semi-supervised Learning
Multiple Players Games

HAL domain(s) :

Informatique [cs]/Intelligence artificielle [cs.AI]

English abstract : [en]

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 ...
Show more >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 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 spectral 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 order relations whereas it is not true for hypergraphs as shown in~\cite{Agarwal2006}. In order to prove the potential of the model, we represent multiple players games with hypernode graphs and introduce a novel method to infer skill ratings from 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.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Comment :

Paper accepted for publication at ECML/PKDD 2014

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL