Graph-based Classification on Heterogeneous Information Networks Graph-based Classification on Heterogeneous Information Networks

A heterogeneous information network is a network composed of multiple types of objects and links. Recently, it has been recognized that heterogeneous information networks are prevalent in the real world. Sometimes, label information is available for part of the objects. Learning from such labeled and unlabeled data can lead to good knowledge extraction of the hidden network structure. However, although classification on homogeneous networks has been studied over decades, classification on heterogeneous networks has not been explored so far. In this paper, we consider the classification problem on heterogeneous networked data which share a common topic. A novel graph-based regularization framework is proposed to model the link structure in heterogeneous information networks with arbitrary network schema and number of object/link types. Specifically, we explicitly differentiate the multi-typed link information by incorporating it into different relation graphs. Based on that framework, we use the label information on part of the objects to predict labels for all types of unlabeled objects efficiently. Experiments on the DBLP dataset show that our algorithm significantly improves the classification accuracy over existing state-of-the-art methods.