I will motivate 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 classification performance by up to 15% over existing approaches. Furthermore, our approach runs in time proportional to L*E, where L is the number of labeled nodes and E is the number of edges. I will conclude by placing this work in the context of my research program on role discovery in dynamic heterogeneous networks.
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