Mining Differential Hubs in Homogenous Networks

Networks have been extensively used to model various complex systems such as online social networks, co-authorship and citation networks and gene networks. Due to different kinds of variations such as temporal, spatial, topic and phenotypic variations, several variants of the same network may exist. For several practical problems, identifying the nodes that are changing between the networks provide vital information regarding the dynamics of the network states. Given two networks where the nodes are the same in both networks, but the edges are different, we consider the problem of identifying a set of hubs that best explain the differences between the two networks. To the best of our knowledge, this is the first work to address the problem of finding the differential hubs. To address this problem, we propose a novel ranking algorithm, DiffRank, which ranks the nodes of two networks based on their differential behavior between the two networks. We define new measures such as differential connectivity and differential centrality for each node. These measures are propagated through the network and are optimized to capture the local and global structural changes between two networks. We demonstrate the effectiveness of DiffRank on synthetic datasets and real-world applications including collaboration and biological networks. We show that DiffRank identifies meaningful and practically valuable information compared to some of the baseline methods that can be used for such a task.