Affiliation recommendation using auxiliary networks
Proceedings of the fourth ACM conference on Recommender systems
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Multi task learning on multiple related networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
TAGs: scalable threshold-based algorithms for proximity computation in graphs
Proceedings of the 14th International Conference on Extending Database Technology
Scalable Affiliation Recommendation using Auxiliary Networks
ACM Transactions on Intelligent Systems and Technology (TIST)
Semi-supervised classification based on random subspace dimensionality reduction
Pattern Recognition
Semi-supervised ensemble classification in subspaces
Applied Soft Computing
A bootstrapping method for learning from heterogeneous data
FGIT'11 Proceedings of the Third international conference on Future Generation Information Technology
Community detection via heterogeneous interaction analysis
Data Mining and Knowledge Discovery
An architecture to efficiently learn co-similarities from multi-view datasets
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
Global Similarity in Social Networks with Typed Edges
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Flexible and robust co-regularized multi-domain graph clustering
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient community detection in large networks using content and links
Proceedings of the 22nd international conference on World Wide Web
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In graph-based learning models, entities are often represented as vertices in an undirected graph with weighted edges describing the relationships between entities. In many real-world applications, however, entities are often associated with relations of different types and/or from different sources, which can be well captured by multiple undirected graphs over the same set of vertices. How to exploit such multiple sources of information to make better inferences on entities remains an interesting open problem. In this paper, we focus on the problem of clustering the vertices based on multiple graphs in both unsupervised and semi-supervised settings. As one of our contributions, we propose Linked Matrix Factorization (LMF) as a novel way of fusing information from multiple graph sources. In LMF, each graph is approximated by matrix factorization with a graph-specific factor and a factor common to all graphs, where the common factor provides features for all vertices. Experiments on SIAM journal data show that (1) we can improve the clustering accuracy through fusing multiple sources of information with several models, and (2) LMF yields superior or competitive results compared to other graph-based clustering methods.