Mult iTas kLearnin go nMultipl eRelate dNetworks

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.