Learning from labeled and unlabeled data on a directed graph
ICML '05 Proceedings of the 22nd international conference on Machine learning
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Ranking-based clustering of heterogeneous information networks with star network schema
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Mining topic-level influence in heterogeneous networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Graph regularized transductive classification on heterogeneous information networks
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Ranking-based classification of heterogeneous information networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-relational Link Prediction in Heterogeneous Information Networks
ASONAM '11 Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining
Classification and annotation in social corpora using multiple relations
Proceedings of the 20th ACM international conference on Information and knowledge management
Relation strength-aware clustering of heterogeneous information networks with incomplete attributes
Proceedings of the VLDB Endowment
Community detection via heterogeneous interaction analysis
Data Mining and Knowledge Discovery
Meta path-based collective classification in heterogeneous information networks
Proceedings of the 21st ACM international conference on Information and knowledge management
User guided entity similarity search using meta-path selection in heterogeneous information networks
Proceedings of the 21st ACM international conference on Information and knowledge management
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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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.