Using ghost edges for classification in sparsely labeled networks

We address the problem of classification in partially labeled networks (a.k.a. within-network classification) where observed class labels are sparse. Techniques for statistical relational learning have been shown to perform well on network classification tasks by exploiting dependencies between class labels of neighboring nodes. However, relational classifiers can fail when unlabeled nodes have too few labeled neighbors to support learning (during training phase) and/or inference (during testing phase). This situation arises in realworld problems when observed labels are sparse. In this paper, we propose a novel approach to within-network classification that combines aspects of statistical relational learning and semi-supervised learning to improve classification performance in sparse networks. Our approach works by adding "ghost edges" to a network, which enable the flow of information from labeled to unlabeled nodes. Through experiments on real-world data sets, we demonstr...

• Type: Conference
• Year: 2008
• Where: KDD
• Authors: Brian Gallagher, Hanghang Tong, Tina Eliassi-Rad, Christos Faloutsos
• Keywords: Data Mining, KDD 2008, Semi-supervised Learning, Statistical Relational Learning, Unlabeled Nodes