Multilabel classification on heterogeneous graphs with gaussian embeddings

Ludovic Dos Santos 1 Benjamin Piwowarski 2 Patrick Gallinari 1
1 MLIA - Machine Learning and Information Access
LIP6 - Laboratoire d'Informatique de Paris 6
2 BD - Bases de Données
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : We consider the problem of node classification in heterogeneous graphs where both nodes and relations may be of different types and a different set of categories is associated to each node type. When graph node classification has mainly been addressed for homogeneous graphs, heterogeneous classification is a recent problem which has been motivated by applications in fields such as social networks where the graphs are intrinsically heterogeneous. We propose a transductive approach to this problem based on learning graph embeddings and model the uncertainty associated to the node representations using Gaussian embeddings. A comparison with representative baselines is provided on three heterogeneous datasets.
Type de document :
Communication dans un congrès
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery, Sep 2016, Riva del garda, Italy. ECML-PKDD 2016, 2016, 〈http://www.ecmlpkdd2016.org/〉
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Soumis le : mercredi 10 août 2016 - 15:30:32
Dernière modification le : jeudi 11 janvier 2018 - 06:27:19

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  • HAL Id : hal-01352911, version 1

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Ludovic Dos Santos, Benjamin Piwowarski, Patrick Gallinari. Multilabel classification on heterogeneous graphs with gaussian embeddings. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery, Sep 2016, Riva del garda, Italy. ECML-PKDD 2016, 2016, 〈http://www.ecmlpkdd2016.org/〉. 〈hal-01352911〉

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