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Node classification in attributed graphs is an important task in multiplepractical settings, but it can often be difficult or expensive to obtainlabels. Active learning can improve the achieved classification performance fora given budget on the number of queried labels. The best existing methods arebased on graph neural networks, but they often perform poorly unless a sizeablevalidation set of labelled nodes is available in order to choose goodhyperparameters. We propose a novel graph-based active learning algorithm forthe task of node classification in attributed graphs; our algorithm uses graphcognizant logistic regression, equivalent to a linearized graph convolutionalneural network (GCN), for the prediction phase and maximizes the expected errorreduction in the query phase. To reduce the delay experienced by a labellerinteracting with the system, we derive a preemptive querying system thatcalculates a new query during the labelling process, and to address the settingwhere learning starts with almost no labelled data, we also develop a hybridalgorithm that performs adaptive model averaging of label propagation andlinearized GCN inference. We conduct experiments on five public benchmarkdatasets, demonstrating a significant improvement over state-of-the-artapproaches and illustrate the practical value of the method by applying it to aprivate microwave link network dataset.

Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation