Selective sampling on graphs for classification
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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Active learning on graphs has received increasing interest in the past years. In this paper, we propose a \textit{nonadaptive} active learning approach on graphs, based on generalization error bound minimization. In particular, we present a data-dependent error bound for a graph-based learning method, namely learning with local and global consistency (LLGC). We show that the empirical transductive Rademacher complexity of the function class for LLGC provides a natural criterion for active learning. The resulting active learning approach is to select a subset of nodes on a graph such that the empirical transductive Rademacher complexity of LLGC is minimized. We propose a simple yet effective sequential optimization algorithm to solve it. Experiments on benchmark datasets show that the proposed method outperforms the state-of-the-art active learning methods on graphs.