UCLA

Network clustering is an important problem that has recently drawn a lot of attentions. Most existing work focuses on clustering nodes within a single network. In many applications, however, there exist multiple related networks, in which each network may be constructed from a different domain and instances in one domain may be related to instances in other domains. In this paper, we propose a robust algorithm, MCA, for multi-network clustering that takes into account cross-domain relationships between instances. MCA has several advantages over the existing single network clustering methods. First, it is able to detect associations between clusters from different domains, which, however, is not addressed by any existing methods. Second, it achieves more consistent clustering results on multiple networks by leveraging the duality between clustering individual networks and inferring cross-network cluster alignment. Finally, it provides a multi-network clustering solution that is more robust to noise and errors. We perform extensive experiments on a variety of real and synthetic networks to demonstrate the effectiveness and efficiency of MCA.