Simultaneous classification and community detection on heterogeneous network data

Abstract

Previous studies on network mining have focused primarily on learning a single task (such as classification or community detection) on a given network. This paper considers the problem of multi-task learning on heterogeneous network data. Specifically, we present a novel framework that enables one to perform classification on one network and community detection in another related network. Multi-task learning is accomplished by introducing a joint objective function that must be optimized to ensure the classes in one network are consistent with the link structure, nodal attributes, as well as the communities detected in another network. We provide both theoretical and empirical analysis of the framework. We also show that the framework can be extended to incorporate prior information about the correspondences between the clusters and classes in different networks. Experiments performed on both real-world and synthetic data sets demonstrate the effectiveness of the joint framework compared to applying classification and community detection algorithms on each network separately.