Learning latent representations of nodes for classifying in heterogeneous social networks

Yann Jacob Ludovic Denoyer 1 Patrick Gallinari 1
1 MLIA - Machine Learning and Information Access
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : Social networks are heterogeneous systems composed of different types of nodes (e.g. users, content, groups, etc.) and relations (e.g. social or similarity relations). While learning and performing inference on homogeneous networks have motivated a large amount of research, few work exists on heterogeneous networks and there are open and challenging issues for existing methods that were previously developed for homogeneous networks. We address here the specific problem of nodes classification and tagging in heterogeneous social networks, where different types of nodes are considered, each type with its own label or tag set. We propose a new method for learning node representations onto a latent space, common to all the different node types. Inference is then performed in this latent space. In this framework, two nodes connected in the network will tend to share similar representations regardless of their types. This allows bypassing limitations of the methods based on direct extensions of homogenous frameworks and exploiting the dependencies and correlations between the different node types. The proposed method is tested on two representative datasets and compared to state-of-the-art methods and to baselines.
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Yann Jacob, Ludovic Denoyer, Patrick Gallinari. Learning latent representations of nodes for classifying in heterogeneous social networks. The 7th ACM international conference on Web search and data mining, Feb 2014, New York City, United States. pp.373--382, ⟨10.1145/2556195.2556225⟩. ⟨hal-01212733⟩



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