  What target labels are most effective for graph neural network (GNN)
training? In some applications where GNNs excel-like drug design or fraud
detection, labeling new instances is expensive. We develop a data-efficient
active sampling framework, ScatterSample, to train GNNs under an active
learning setting. ScatterSample employs a sampling module termed
DiverseUncertainty to collect instances with large uncertainty from different
regions of the sample space for labeling. To ensure diversification of the
selected nodes, DiverseUncertainty clusters the high uncertainty nodes and
selects the representative nodes from each cluster. Our ScatterSample algorithm
is further supported by rigorous theoretical analysis demonstrating its
advantage compared to standard active sampling methods that aim to simply
maximize the uncertainty and not diversify the samples. In particular, we show
that ScatterSample is able to efficiently reduce the model uncertainty over the
whole sample space. Our experiments on five datasets show that ScatterSample
significantly outperforms the other GNN active learning baselines, specifically
it reduces the sampling cost by up to 50% while achieving the same test
accuracy.
