Year: 2010
Type: report
Abstract: 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 part of the objects. Learning from such labeled and unlabeled data via classification 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 until recently. In this paper, we consider the transductive classification problem on heterogeneous networked data which share a common topic. Only part of the 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, GNetClass, is proposed to model the link structure in information networks with arbitrary network schema and number of object/link types. Specifically, we explicitly respect the type differences by preserving consistency over each relation graph corresponding to each type of links separately. Efficient computational schemes are then introduced to solve the corresponding optimization problem. Experiments on the DBLP data set show that our algorithm significantly improves the classification accuracy over existing state-of-the-art methods.
Authors: Ji Ming, Yizhou Sun, Marina Danilevsky, Jiawei Han
Institution: University of Illinois Urbana-Champaign
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2218611869