Hypernode Graphs for Learning from Binary ...
Document type :
Rapport de recherche: Autre communication scientifique (congrès sans actes - poster - séminaire...)
Title :
Hypernode Graphs for Learning from Binary Relations between Groups in Networks
Author(s) :
Ricatte, Thomas [Auteur]
Machine Learning in Information Networks [MAGNET]
Gilleron, Remi [Auteur]
Machine Learning in Information Networks [MAGNET]
Université de Lille
Tommasi, Marc [Auteur]
Machine Learning in Information Networks [MAGNET]
Université de Lille
Machine Learning in Information Networks [MAGNET]
Gilleron, Remi [Auteur]

Machine Learning in Information Networks [MAGNET]
Université de Lille
Tommasi, Marc [Auteur]

Machine Learning in Information Networks [MAGNET]
Université de Lille
Institution :
INRIA Lille
Publication date :
2015-01-29
English keyword(s) :
Semi-supervised Learning
Graph Laplacians
Graph Kernels
Spectral Learning
Skill Rating Algorithms
Hypergraphs
Graph Laplacians
Graph Kernels
Spectral Learning
Skill Rating Algorithms
Hypergraphs
HAL domain(s) :
Informatique [cs]/Apprentissage [cs.LG]
Statistiques [stat]/Autres [stat.ML]
Statistiques [stat]/Autres [stat.ML]
English abstract : [en]
The aim of this paper is to propose methods for learning from interactions between groups in networks. We propose a proper extension of graphs, called hypernode graphs as a formal tool able to model group interactions. A ...
Show more >The aim of this paper is to propose methods for learning from interactions between groups in networks. We propose a proper extension of graphs, called hypernode graphs as a formal tool able to model group interactions. A hypernode graph is a collection of weighted relations between two disjoint groups of nodes. Weights quantify the individual participation of nodes to a given relation. We define Laplacians and kernels for hypernode graphs and prove that they strictly generalize over graph kernels and hypergraph kernels. We then proceed to prove that hypernode graphs correspond to signed graphs such that the matrix D − W is positive semi-definite. As a consequence, homophilic relations between groups may lead to non homophilic relations between individuals. We also define the notion of connected hypernode graphs and a resistance distance for connected hypernode graphs. Then, we propose spectral learning algorithms on hypernode graphs allowing to infer node ratings or node labelings. As a proof of concept, we model multiple players games with hypernode graphs and we define skill rating algorithms competitive with specialized algorithms.Show less >
Show more >The aim of this paper is to propose methods for learning from interactions between groups in networks. We propose a proper extension of graphs, called hypernode graphs as a formal tool able to model group interactions. A hypernode graph is a collection of weighted relations between two disjoint groups of nodes. Weights quantify the individual participation of nodes to a given relation. We define Laplacians and kernels for hypernode graphs and prove that they strictly generalize over graph kernels and hypergraph kernels. We then proceed to prove that hypernode graphs correspond to signed graphs such that the matrix D − W is positive semi-definite. As a consequence, homophilic relations between groups may lead to non homophilic relations between individuals. We also define the notion of connected hypernode graphs and a resistance distance for connected hypernode graphs. Then, we propose spectral learning algorithms on hypernode graphs allowing to infer node ratings or node labelings. As a proof of concept, we model multiple players games with hypernode graphs and we define skill rating algorithms competitive with specialized algorithms.Show less >
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Anglais
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