  Active learning (AL) on attributed graphs has received increasing attention
with the prevalence of graph-structured data. Although AL has been widely
studied for alleviating label sparsity issues with the conventional non-related
data, how to make it effective over attributed graphs remains an open research
question. Existing AL algorithms on graphs attempt to reuse the classic AL
query strategies designed for non-related data. However, they suffer from two
major limitations. First, different AL query strategies calculated in distinct
scoring spaces are often naively combined to determine which nodes to be
labelled. Second, the AL query engine and the learning of the classifier are
treated as two separating processes, resulting in unsatisfactory performance.
In this paper, we propose a SEmi-supervised Adversarial active Learning (SEAL)
framework on attributed graphs, which fully leverages the representation power
of deep neural networks and devises a novel AL query strategy in an adversarial
way. Our framework learns two adversarial components: a graph embedding network
that encodes both the unlabelled and labelled nodes into a latent space,
expecting to trick the discriminator to regard all nodes as already labelled,
and a semi-supervised discriminator network that distinguishes the unlabelled
from the existing labelled nodes in the latent space. The divergence score,
generated by the discriminator in a unified latent space, serves as the
informativeness measure to actively select the most informative node to be
labelled by an oracle. The two adversarial components form a closed loop to
mutually and simultaneously reinforce each other towards enhancing the active
learning performance. Extensive experiments on four real-world networks
validate the effectiveness of the SEAL framework with superior performance
improvements to state-of-the-art baselines.
