# Semi-supervised Learning on Graphs with Generative Adversarial Nets

arXiv: https://arxiv.org/abs/1809.00130 · HTML: https://mlsanity.com/paper/1809.00130/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2018.

## Built on by (most cited first, 21 of 21)

- [Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks](https://mlsanity.com/paper/2112.14936/) (2021)
- [DropMessage: Unifying Random Dropping for Graph Neural Networks](https://mlsanity.com/paper/2204.10037/) (2022)
- [FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learning](https://mlsanity.com/paper/2404.14061/) (2024)
- [GRAND+: Scalable Graph Random Neural Networks](https://mlsanity.com/paper/2203.06389/) (2022)
- [RoSGAS: Adaptive Social Bot Detection with Reinforced Self-Supervised GNN Architecture Search](https://mlsanity.com/paper/2206.06757/) (2022)
- [Graph Contrastive Learning with Personalized Augmentation](https://mlsanity.com/paper/2209.06560/) (2022)
- [Half-Hop: A graph upsampling approach for slowing down message passing](https://mlsanity.com/paper/2308.09198/) (2023)
- [Automated Urban Planning for Reimagining City Configuration via Adversarial Learning: Quantification, Generation, and Evaluation](https://mlsanity.com/paper/2112.14699/) (2021)
- [Stable Prediction on Graphs with Agnostic Distribution Shift](https://mlsanity.com/paper/2110.03865/) (2021)
- [Calibrating and Improving Graph Contrastive Learning](https://mlsanity.com/paper/2101.11525/) (2021)
- [A Deep Graph Wavelet Convolutional Neural Network for Semi-supervised Node Classification](https://mlsanity.com/paper/2102.09780/) (2021)
- [EDoG: Adversarial Edge Detection For Graph Neural Networks](https://mlsanity.com/paper/2212.13607/) (2022)
- [A Class-Aware Representation Refinement Framework for Graph Classification](https://mlsanity.com/paper/2209.00936/) (2022)
- [Semi-Supervised Heterogeneous Graph Learning with Multi-level Data Augmentation](https://mlsanity.com/paper/2212.00024/) (2022)
- [Temporal Graph Network Embedding with Causal Anonymous Walks Representations](https://mlsanity.com/paper/2108.08754/) (2021)
- [Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation](https://mlsanity.com/paper/2507.07621/) (2025)
- [Customizing Graph Neural Networks using Path Reweighting](https://mlsanity.com/paper/2106.10866/) (2021)
- [SETTI: A Self-supervised Adversarial Malware Detection Architecture in an IoT Environment](https://mlsanity.com/paper/2204.07772/) (2022)
- [Predicting Financial Literacy via Semi-supervised Learning](https://mlsanity.com/paper/2312.10984/) (2023)
- [CGGM: A conditional graph generation model with adaptive sparsity for node anomaly detection in IoT networks](https://mlsanity.com/paper/2402.17363/) (2024)
- [Constraint Multi-class Positive and Unlabeled Learning for Distantly Supervised Named Entity Recognition](https://mlsanity.com/paper/2504.04963/) (2025)

## Abstract (arXiv)

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.