  This paper studies active learning (AL) on graphs, whose purpose is to
discover the most informative nodes to maximize the performance of graph neural
networks (GNNs). Previously, most graph AL methods focus on learning node
representations from a carefully selected labeled dataset with large amount of
unlabeled data neglected. Motivated by the success of contrastive learning
(CL), we propose a novel paradigm that seamlessly integrates graph AL with CL.
While being able to leverage the power of abundant unlabeled data in a
self-supervised manner, nodes selected by AL further provide semantic
information that can better guide representation learning. Besides, previous
work measures the informativeness of nodes without considering the neighborhood
propagation scheme of GNNs, so that noisy nodes may be selected. We argue that
due to the smoothing nature of GNNs, the central nodes from homophilous
subgraphs should benefit the model training most. To this end, we present a
minimax selection scheme that explicitly harnesses neighborhood information and
discover homophilous subgraphs to facilitate active selection. Comprehensive,
confounding-free experiments on five public datasets demonstrate the
superiority of our method over state-of-the-arts.
