Using ghost edges for classification in sparsely labeled networks

This paper proposes 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 and demonstrates that this approach performs well across a range of conditions where existing approaches fail.

Abstract

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 real-world problems when observed labels are sparse.