Learning from labeled and unlabeled data on a directed graph
ICML '05 Proceedings of the 22nd international conference on Machine learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Linear prediction models with graph regularization for web-page categorization
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast protein classification with multiple networks
Bioinformatics
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Combining Collective Classification and Link Prediction
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Learning to rank relational objects and its application to web search
Proceedings of the 17th international conference on World Wide Web
Annotating photo collections by label propagation according to multiple similarity cues
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Error bounds of multi-graph regularized semi-supervised classification
Information Sciences: an International Journal
Applying Link-Based Classification to Label Blogs
Advances in Web Mining and Web Usage Analysis
Simulated Iterative Classification A New Learning Procedure for Graph Labeling
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Unified video annotation via multigraph learning
IEEE Transactions on Circuits and Systems for Video Technology
Iterative Annotation of Multi-relational Social Networks
ASONAM '10 Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining
Learning latent representations of nodes for classifying in heterogeneous social networks
Proceedings of the 7th ACM international conference on Web search and data mining
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We consider the problem of learning to annotate documents with concepts or keywords in content information networks, where the documents may share multiple relations. The concepts associated to a document will depend both on its content and on its neighbors in the network through the different relations. We formalize this problem as single and multi-label classification in a multi-graph, the nodes being the documents and the edges representing the different relations. The proposed algorithm learns to weight the different relations according to their importance for the annotation task. We perform experiments on different corpora corresponding to different annotation tasks on scientific articles, emails and Flickr images and show how the model may take advantage of the rich relational information.