  Node classification in attributed graphs is an important task in multiple
practical settings, but it can often be difficult or expensive to obtain
labels. Active learning can improve the achieved classification performance for
a given budget on the number of queried labels. The best existing methods are
based on graph neural networks, but they often perform poorly unless a sizeable
validation set of labelled nodes is available in order to choose good
hyperparameters. We propose a novel graph-based active learning algorithm for
the task of node classification in attributed graphs; our algorithm uses graph
cognizant logistic regression, equivalent to a linearized graph convolutional
neural network (GCN), for the prediction phase and maximizes the expected error
reduction in the query phase. To reduce the delay experienced by a labeller
interacting with the system, we derive a preemptive querying system that
calculates a new query during the labelling process, and to address the setting
where learning starts with almost no labelled data, we also develop a hybrid
algorithm that performs adaptive model averaging of label propagation and
linearized GCN inference. We conduct experiments on five public benchmark
datasets, demonstrating a significant improvement over state-of-the-art
approaches and illustrate the practical value of the method by applying it to a
private microwave link network dataset.
