Graph Data Augmentation Papers

This repository contains a list of papers on the Graph Data Augmentation, we categorize them based on their learning objectives and tasks.

We will try to make this list updated. If you found any error or any missed paper, please don't hesitate to open an issue or pull request.

Survey paper

Graph Data Augmentation for Graph Machine Learning: A Survey.

Tong Zhao, Gang Liu, Stephan Günneman, and Meng Jiang.

If you find this repository helpful for your work, please kindly cite our paper.

@article{zhao2022graph,
  title={Graph Data Augmentation for Graph Machine Learning: A Survey},
  author={Zhao, Tong and Liu, Gang and Günneman, Stephan and Jiang, Meng},
  journal={arXiv preprint arXiv:2202.08871},
  year={2022}
}

Graph data augmentation for (semi-)supervised learning

Node-level tasks

• Training Robust Graph Neural Networks with Topology Adaptive Edge Dropping, in arxiv 2021. [pdf]

• FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation Learning, in arXiv 2021. [pdf] [code]

• Semi-Supervised and Self-Supervised Classification with Multi-View Graph Neural Networks, in CIKM 2021. [pdf]

• Metropolis-Hastings Data Augmentation for Graph Neural Networks, in NeurIPS 2021. [pdf]

• Action Sequence Augmentation for Early Graph-based Anomaly Detection, in CIKM 2021. [pdf] [code]

• Local Augmentation for Graph Neural Networks, in arXiv 2021. [pdf]

• Data Augmentation for Graph Neural Networks, in AAAI 2021. [pdf] [code]

• Topological Regularization for Graph Neural Networks Augmentation, in arXiv 2021. [pdf]

• Automated Graph Representation Learning for Node Classification, in IJCNN 2021. [pdf]

• Mixup for Node and Graph Classification, in The WebConf 2021. [pdf] [code]

• Robust Graph Representation Learning via Neural Sparsification, in ICML 2020. [pdf]

• DropEdge: Towards Deep Graph Convolutional Networks on Node Classification, in ICLR 2020. [pdf] [code]

• Graph Structure Learning for Robust Graph Neural Networks, in KDD 2020. [pdf] [code]

• Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View, in AAAI 2020. [pdf]

• FLAG: Adversarial Data Augmentation for Graph Neural Networks, in arXiv 2020. [pdf] [code]

• GraphMix: Improved Training of GNNs for Semi-Supervised Learning, in arXiv 2020. [pdf] [code]

• Diffusion Improves Graph Learning, in NeurIPS 2019. [pdf] [code]

Graph-level tasks

• Graph Transplant: Node Saliency-Guided Graph Mixup with Local Structure Preservation, in AAAI 2022. [pdf]

• ifMixup: Towards Intrusion-Free Graph Mixup for Graph Classification, in arXiv, 2021. [pdf]

• Mixup for Node and Graph Classification, in The WebConf 2021. [pdf] [code]

• MoCL: Data-driven Molecular Fingerprint via Knowledge-aware Contrastive Learning from Molecular Graph, in KDD 2021. [pdf] [code]

• M-Evolve: Structural-Mapping-Based Data Augmentation for Graph Classification, in CIKM 2020 [pdf] and IEEE TNSE 2021. [pdf]

• GraphCrop: Subgraph Cropping for Graph Classification, in arXiv 2020. [pdf]

• FLAG: Adversarial Data Augmentation for Graph Neural Networks, in arXiv 2020. [pdf] [code]

Edge-level tasks

• Counterfactual Graph Learning for Link Prediction, in arXiv 2021. [pdf] [code]

• Adaptive Data Augmentation on Temporal Graphs, in NeurIPS 2021. [pdf]

• FLAG: Adversarial Data Augmentation for Graph Neural Networks, in arXiv 2020. [pdf] [code]

Graph data augmentation with self-supervised learning objectives

Contrastive learning

• Learning Graph Augmentations to Learn Graph Representations, in arXiv 2022. [pdf] [code]

• Fair Node Representation Learning via Adaptive Data Augmentation, in arXiv 2022. [pdf]

• Large-Scale Representation Learning on Graphs via Bootstrapping, in ICLR 2022. [pdf] [code]

• Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices, in arXiv 2021. [pdf]

• Contrastive Self-supervised Sequential Recommendation with Robust Augmentation, in arXiv 2021. [pdf]

• Collaborative Graph Contrastive Learning: Data Augmentation Composition May Not be Necessary for Graph Representation Learning, in arXiv 2021. [pdf]

• Molecular Graph Contrastive Learning with Parameterized Explainable Augmentations, in BIBM 2021. [pdf]

• Self-Supervised GNN that Jointly Learns to Augment, in NeurIPS Workshop 2021. [pdf]

• InfoGCL: Information-Aware Graph Contrastive Learning, in NeurIPS 2021. [pdf]

• Adversarial Graph Augmentation to Improve Graph Contrastive Learning, in NeurIPS 2021. [pdf] [code]

• Graph Contrastive Learning with Adaptive Augmentation, in The WebConf 2021. [pdf] [code]

• Semi-Supervised and Self-Supervised Classification with Multi-View Graph Neural Networks, in CIKM 2021. [pdf]

• Graph Contrastive Learning Automated, in ICML 2021. [pdf] [code]

• Graph Data Augmentation based on Adaptive Graph Convolution for Skeleton-based Action Recognition, in ICCSNT 2021. [pdf]

• Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning, in ICDM 2020. [pdf] [code]

• Contrastive Multi-View Representation Learning on Graphs, in ICML 2020. [pdf] [code]

• Graph Contrastive Learning with Augmentations, in NeurIPS 2020. [pdf] [code]

• Deep Graph Contrastive Representation Learning, in GRL+ Workshop @ICML 2020. [pdf] [code]

• Deep Graph Infomax, in ICLR 2019. [pdf] [code]

Consistency learning

• NodeAug: Semi-Supervised Node Classification with Data Augmentation, in KDD 2020. [pdf]

• Graph Random Neural Network for Semi-Supervised Learning on Graphs, in NeurIPS 2020. [pdf] [code]

Acknowledgement

This page is contributed and maintained by Tong Zhao (tzhao2@nd.edu), Gang Liu (gliu7@nd.edu), and Yingheng Wang (jakewyh@163.com).