# Imbalanced Graph Classification via Graph-of-Graph Neural Networks

arXiv: https://arxiv.org/abs/2112.00238 · HTML: https://mlsanity.com/paper/2112.00238/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 63 references that are in the map.
- First posted on arXiv 1 Dec 2021.

## Builds on (most cited first, 23 of 23)

- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (2019)
- [Graph Contrastive Learning with Augmentations](https://mlsanity.com/paper/2010.13902/) (2020)
- [Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning](https://mlsanity.com/paper/1801.07606/) (2018)
- [InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization](https://mlsanity.com/paper/1908.01000/) (2019)
- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [Towards Deeper Graph Neural Networks](https://mlsanity.com/paper/2007.09296/) (2020)
- [A Fair Comparison of Graph Neural Networks for Graph Classification](https://mlsanity.com/paper/1912.09893/) (2019)
- [Graph Random Neural Network for Semi-Supervised Learning on Graphs](https://mlsanity.com/paper/2005.11079/) (2020)
- [Data Augmentation for Graph Neural Networks](https://mlsanity.com/paper/2006.06830/) (2020)
- [GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks](https://mlsanity.com/paper/2103.08826/) (2021)
- [Unifying Graph Convolutional Neural Networks and Label Propagation](https://mlsanity.com/paper/2002.06755/) (2020)
- [Graph Prototypical Networks for Few-shot Learning on Attributed Networks](https://mlsanity.com/paper/2006.12739/) (2020)
- [Graph-based Semi-supervised Learning: A Comprehensive Review](https://mlsanity.com/paper/2102.13303/) (2021)
- [ImGAGN:Imbalanced Network Embedding via Generative Adversarial Graph Networks](https://mlsanity.com/paper/2106.02817/) (2021)
- [What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?](https://mlsanity.com/paper/1801.06397/) (2018)
- [GoGNN: Graph of Graphs Neural Network for Predicting Structured Entity Interactions](https://mlsanity.com/paper/2005.05537/) (2020)
- [Nearly Optimal Regret for Stochastic Linear Bandits with Heavy-Tailed Payoffs](https://mlsanity.com/paper/2004.13465/) (2020)
- [Network Embedding with Completely-imbalanced Labels](https://mlsanity.com/paper/2007.03545/) (2020)
- [Distance-wise Prototypical Graph Neural Network in Node Imbalance Classification](https://mlsanity.com/paper/2110.12035/) (2021)

## Built on by (most cited first, 27 of 27)

- [A Comprehensive Survey on Deep Graph Representation Learning](https://mlsanity.com/paper/2304.05055/) (2023)
- [A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges](https://mlsanity.com/paper/2403.04468/) (2024)
- [Class-Imbalanced Learning on Graphs: A Survey](https://mlsanity.com/paper/2304.04300/) (2023)
- [A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications](https://mlsanity.com/paper/2308.16375/) (2023)
- [A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions](https://mlsanity.com/paper/2308.13821/) (2023)
- [Towards Data-centric Graph Machine Learning: Review and Outlook](https://mlsanity.com/paper/2309.10979/) (2023)
- [Causality and Independence Enhancement for Biased Node Classification](https://mlsanity.com/paper/2310.09586/) (2023)
- [TopoImb: Toward Topology-level Imbalance in Learning from Graphs](https://mlsanity.com/paper/2212.08689/) (2022)
- [Cost Sensitive GNN-based Imbalanced Learning for Mobile Social Network Fraud Detection](https://mlsanity.com/paper/2303.17486/) (2023)
- [A Topological Perspective on Demystifying GNN-Based Link Prediction Performance](https://mlsanity.com/paper/2310.04612/) (2023)
- [A Comprehensive Survey on Imbalanced Data Learning](https://mlsanity.com/paper/2502.08960/) (2025)
- [Mastering Long-Tail Complexity on Graphs: Characterization, Learning, and Generalization](https://mlsanity.com/paper/2305.09938/) (2023)
- [GDeR: Safeguarding Efficiency, Balancing, and Robustness via Prototypical Graph Pruning](https://mlsanity.com/paper/2410.13761/) (2024)
- [RAHNet: Retrieval Augmented Hybrid Network for Long-tailed Graph Classification](https://mlsanity.com/paper/2308.02335/) (2023)
- [State of the Art and Potentialities of Graph-level Learning](https://mlsanity.com/paper/2301.05860/) (2023)
- [Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations](https://mlsanity.com/paper/2306.01631/) (2023)
- [Towards Long-Tailed Recognition for Graph Classification via Collaborative Experts](https://mlsanity.com/paper/2308.16609/) (2023)
- [Diving into Unified Data-Model Sparsity for Class-Imbalanced Graph Representation Learning](https://mlsanity.com/paper/2210.00162/) (2022)
- [Edge Classification on Graphs: New Directions in Topological Imbalance](https://mlsanity.com/paper/2406.11685/) (2024)
- [UGMAE: A Unified Framework for Graph Masked Autoencoders](https://mlsanity.com/paper/2402.08023/) (2024)
- [Graph Size-imbalanced Learning with Energy-guided Structural Smoothing](https://mlsanity.com/paper/2412.17591/) (2024)
- [NeuBM: Mitigating Model Bias in Graph Neural Networks through Neutral Input Calibration](https://mlsanity.com/paper/2505.15180/) (2025)
- [SamGoG: A Sampling-Based Graph-of-Graphs Framework for Imbalanced Graph Classification](https://mlsanity.com/paper/2507.13741/) (2025)
- [Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural Networks](https://mlsanity.com/paper/2405.04903/) (2024)
- [Catch Causal Signals from Edges for Label Imbalance in Graph Classification](https://mlsanity.com/paper/2501.01707/) (2025)
- [Knowledge Homophily in Large Language Models](https://mlsanity.com/paper/2509.23773/) (2025)
- [Scaffold-Aware Generative Augmentation and Reranking for Enhanced Virtual Screening](https://mlsanity.com/paper/2510.16306/) (2025)

## Abstract (arXiv)

Graph Neural Networks (GNNs) have achieved unprecedented success in identifying categorical labels of graphs. However, most existing graph classification problems with GNNs follow the protocol of balanced data splitting, which misaligns with many real-world scenarios in which some classes have much fewer labels than others. Directly training GNNs under this imbalanced scenario may lead to uninformative representations of graphs in minority classes, and compromise the overall classification performance, which signifies the importance of developing effective GNNs towards handling imbalanced graph classification. Existing methods are either tailored for non-graph structured data or designed specifically for imbalanced node classification while few focus on imbalanced graph classification. To this end, we introduce a novel framework, Graph-of-Graph Neural Networks (G2GNN), which alleviates the graph imbalance issue by deriving extra supervision globally from neighboring graphs and locally from stochastic augmentations of graphs. Globally, we construct a graph of graphs (GoG) based on kernel similarity and perform GoG propagation to aggregate neighboring graph representations. Locally, we employ topological augmentation via masking node features or dropping edges with self-consistency regularization to generate stochastic augmentations of each graph that improve the model generalibility. Extensive graph classification experiments conducted on seven benchmark datasets demonstrate our proposed G2GNN outperforms numerous baselines by roughly 5% in both F1-macro and F1-micro scores. The implementation of G2GNN is available at https://github.com/YuWVandy/G2GNNhttps://github.com/YuWVandy/G2GNN