  We investigate how generative adversarial nets (GANs) can help
semi-supervised learning on graphs. We first provide insights on working
principles of adversarial learning over graphs and then present GraphSGAN, a
novel approach to semi-supervised learning on graphs. In GraphSGAN, generator
and classifier networks play a novel competitive game. At equilibrium,
generator generates fake samples in low-density areas between subgraphs. In
order to discriminate fake samples from the real, classifier implicitly takes
the density property of subgraph into consideration. An efficient adversarial
learning algorithm has been developed to improve traditional normalized graph
Laplacian regularization with a theoretical guarantee. Experimental results on
several different genres of datasets show that the proposed GraphSGAN
significantly outperforms several state-of-the-art methods. GraphSGAN can be
also trained using mini-batch, thus enjoys the scalability advantage.
