NDLI: Using ghost edges for classification in sparsely labeled networks

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Using ghost edges for classification in sparsely labeled networks

Content Provider	ACM Digital Library
Author	Tong, Hanghang Eliassi-Rad, Tina Gallagher, Brian Faloutsos, Christos
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. 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.
Starting Page	256
Ending Page	264
Page Count	9
File Format	PDF QT / MOV
ISBN	9781605581934
DOI	10.1145/1401890.1401925
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-08-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Statistical relational learning Semi-supervised learning Random walk Collective classification
Content Type	Video Text
Resource Type	Article

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