  The objective of active learning (AL) is to train classification models with
less number of labeled instances by selecting only the most informative
instances for labeling. The AL algorithms designed for other data types such as
images and text do not perform well on graph-structured data. Although a few
heuristics-based AL algorithms have been proposed for graphs, a principled
approach is lacking. In this paper, we propose MetAL, an AL approach that
selects unlabeled instances that directly improve the future performance of a
classification model. For a semi-supervised learning problem, we formulate the
AL task as a bilevel optimization problem. Based on recent work in
meta-learning, we use the meta-gradients to approximate the impact of
retraining the model with any unlabeled instance on the model performance.
Using multiple graph datasets belonging to different domains, we demonstrate
that MetAL efficiently outperforms existing state-of-the-art AL algorithms.
