Representation Learning for Classification in Heterogeneous Graphs with Application to Social Networks

Ludovic Dos Santos 1 Benjamin Piwowarski 2 Ludovic Denoyer 1 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 address the task of node classification in heterogeneous networks, where the nodes are of different types, each type having its own set of labels, and the relations between nodes may also be of different types. A typical example is provided by social networks where node types may for example be users, content, or films, and relations friendship, like, authorship. Learning and performing inference on such heterogeneous networks is a recent task requiring new models and algorithms. We propose a model, Labeling Heterogeneous Network (LaHNet), a transductive approach to classification that learns to project the different types of nodes into a common latent space. This embedding is learned so as to reflect different characteristics of the problem such as the correlation between node labels, as well as the graph topology. The application focus is on social graphs, but the algorithm is general and can be used for other domains. The model is evaluated on five datasets representative of different instances of social data.
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https://hal.archives-ouvertes.fr/hal-01853928
Contributor : Benjamin Piwowarski <>
Submitted on : Monday, August 6, 2018 - 8:36:59 AM
Last modification on : Wednesday, March 27, 2019 - 1:34:32 AM

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Ludovic Dos Santos, Benjamin Piwowarski, Ludovic Denoyer, Patrick Gallinari. Representation Learning for Classification in Heterogeneous Graphs with Application to Social Networks. ACM Transactions on Knowledge Discovery from Data (TKDD), ACM, 2018, 12 (5), pp.1 - 33. ⟨10.1145/3201603⟩. ⟨hal-01853928⟩

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