  Graph Neural Networks (GNNs) have achieved great success in various tasks,
but their performance highly relies on a large number of labeled nodes, which
typically requires considerable human effort. GNN-based Active Learning (AL)
methods are proposed to improve the labeling efficiency by selecting the most
valuable nodes to label. Existing methods assume an oracle can correctly
categorize all the selected nodes and thus just focus on the node selection.
However, such an exact labeling task is costly, especially when the
categorization is out of the domain of individual expert (oracle). The paper
goes further, presenting a soft-label approach to AL on GNNs. Our key
innovations are: i) relaxed queries where a domain expert (oracle) only judges
the correctness of the predicted labels (a binary question) rather than
identifying the exact class (a multi-class question), and ii) new criteria of
maximizing information gain propagation for active learner with relaxed queries
and soft labels. Empirical studies on public datasets demonstrate that our
method significantly outperforms the state-of-the-art GNN-based AL methods in
terms of both accuracy and labeling cost.
