  Many score-based active learning methods have been successfully applied to
graph-structured data, aiming to reduce the number of labels and achieve better
performance of graph neural networks based on predefined score functions.
However, these algorithms struggle to learn policy distributions that are
proportional to rewards and have limited exploration capabilities. In this
paper, we innovatively formulate the graph active learning problem as a
generative process, named GFlowGNN, which generates various samples through
sequential actions with probabilities precisely proportional to a predefined
reward function. Furthermore, we propose the concept of flow nodes and flow
features to efficiently model graphs as flows based on generative flow
networks, where the policy network is trained with specially designed rewards.
Extensive experiments on real datasets show that the proposed approach has good
exploration capability and transferability, outperforming various
state-of-the-art methods.
