Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Learning multiple graphs for document recommendations
Proceedings of the 17th international conference on World Wide Web
A co-classification framework for detecting web spam and spammers in social media web sites
Proceedings of the 18th ACM conference on Information and knowledge management
Clustering with Multiple Graphs
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
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With the rapid proliferation of online social networks, the need for newer class of learning algorithm to simultaneously deal with multiple related networks has become increasingly important. This paper proposes an approach for multi-task learning in multiple related networks, where in we perform different tasks such as classification on one network and clustering on the other. We show that the framework can be extended to incorporate prior information about the correspondences between the clusters and classes in different networks. We have performed experiments on real-world data sets to demonstrate the effectiveness of the proposed framework.