SEAL trains a graph embedding network and a semisupervised discriminator adversarially so that the discriminator's divergence score selects which unlabeled nodes to label, improving node classification accuracy over prior active learning baselines.
This offers an advantage that the graph embedding network and the discriminator can collaborate with each other to mutually strengthen their performance
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SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs
SEAL trains a graph embedding network and a semisupervised discriminator adversarially so that the discriminator's divergence score selects which unlabeled nodes to label, improving node classification accuracy over prior active learning baselines.