Semi-supervised Learning on Graphs with Generative Adversarial Nets
This addresses the problem of improving classification accuracy in graph-based semi-supervised learning for domains like social networks or citation graphs, with incremental advancements over existing methods.
The paper tackles semi-supervised learning on graphs by proposing GraphSGAN, which uses generative adversarial nets to generate fake samples in low-density areas between subgraphs, and it significantly outperforms state-of-the-art methods on various datasets.
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.