document: 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 real-world 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 demonstrate that our approach performs well across a range of conditions where existing approaches , such as collective classification and semi-supervised learning , fail . On all tasks , our approach improves area under the ROC curve ( AUC ) by up to 15 points over existing approaches . Furthermore , we demonstrate that our approach runs in time proportional to L • E , where L is the number of labeled nodes and E is the number of edges .
doc_bio_tags: O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O