Multi network clustering using k-means

Joint clustering of multiple networks has been shown to be much more accurate when compared to the clustering on individual networks performed separately. For joint multi-network clustering, many multi view and multidomain network clustering methods have been proposed. These methods assume that there is a common clustering structure which is shared by all networks, and different networks can provide complementary information on the underlying clustering structure. Better clustering performance can be achieved by considering the groups differently. As a result, an ideal method should be able to automatically detect network groups so that networks which are in the same group share a common clustering structure. To address this problem, we propose a novel method, K-means to simultaneously group and cluster multiple networks.