Graph Regularized Transductive 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 strongly-typed heterogeneous information networks are prevalent in the real world. Sometimes, label information is available for some objects. Learning from such labeled and unlabeled data via transductive classification can lead to good knowledge extraction of the hidden network structure. However, although classification on homogeneous networks has been studied for decades, classification on heterogeneous networks has not been explored until recently. In this paper, we consider the transductive classification problem on heterogeneous networked data which share a common topic. Only some objects in the given network are labeled, and we aim to predict labels for all types of the remaining objects. A novel graph-based regularization framework, GNetMine, is proposed to model the link structure in information networks with arbitrary network schema and arbi...

• Type: Journal
• Year: 2010
• Where: PKDD
• Authors: Ming Ji, Yizhou Sun, Marina Danilevsky, Jiawei Han, Jing Gao
• Keywords: Data Mining, Heterogeneous Information Networks, PKDD 2010, Transductive Classification, Transductive Classification Problem