# Graph Neural Network with Curriculum Learning for Imbalanced Node Classification

arXiv: https://arxiv.org/abs/2202.02529 · HTML: https://mlsanity.com/paper/2202.02529/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 5 Feb 2022.

## Builds on (most cited first, 9 of 9)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Spectral Networks and Locally Connected Networks on Graphs](https://mlsanity.com/paper/1312.6203/) (2013)
- [Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View](https://mlsanity.com/paper/1909.03211/) (2019)
- [GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks](https://mlsanity.com/paper/2103.08826/) (2021)
- [Dynamic Curriculum Learning for Imbalanced Data Classification](https://mlsanity.com/paper/1901.06783/) (2019)
- [Imbalanced Deep Learning by Minority Class Incremental Rectification](https://mlsanity.com/paper/1804.10851/) (2018)

## Built on by (most cited first, 19 of 19)

- [Data Augmentation for Deep Graph Learning: A Survey](https://mlsanity.com/paper/2202.08235/) (2022)
- [Graph Data Augmentation for Graph Machine Learning: A Survey](https://mlsanity.com/paper/2202.08871/) (2022)
- [A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges](https://mlsanity.com/paper/2403.04468/) (2024)
- [HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction](https://mlsanity.com/paper/2205.02225/) (2022)
- [Class-Imbalanced Graph Learning without Class Rebalancing](https://mlsanity.com/paper/2308.14181/) (2023)
- [Class-Imbalanced Learning on Graphs: A Survey](https://mlsanity.com/paper/2304.04300/) (2023)
- [Data-centric Graph Learning: A Survey](https://mlsanity.com/paper/2310.04987/) (2023)
- [Curriculum Graph Machine Learning: A Survey](https://mlsanity.com/paper/2302.02926/) (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)
- [Curriculum Learning for Graph Neural Networks: Which Edges Should We Learn First](https://mlsanity.com/paper/2310.18735/) (2023)
- [CEGA: A Cost-Effective Approach for Graph-Based Model Extraction and Acquisition](https://mlsanity.com/paper/2506.17709/) (2025)
- [AGSOA:Graph Neural Network Targeted Attack Based on Average Gradient and Structure Optimization](https://mlsanity.com/paper/2406.13228/) (2024)
- [Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning](https://mlsanity.com/paper/2507.09132/) (2025)
- [GraphIFE: Rethinking Graph Imbalance Node Classification via Invariant Learning](https://mlsanity.com/paper/2509.23616/) (2025)
- [Rel-MOSS: Towards Imbalanced Relational Deep Learning on Relational Databases](https://mlsanity.com/paper/2603.07916/) (2026)
- [UPL: Uncertainty-aware Pseudo-labeling for Imbalance Transductive Node Classification](https://mlsanity.com/paper/2502.00716/) (2025)
- [Adversarial Curriculum Graph-Free Knowledge Distillation for Graph Neural Networks](https://mlsanity.com/paper/2504.00540/) (2025)
- [Cooperative Causal GraphSAGE](https://mlsanity.com/paper/2505.14748/) (2025)

## Abstract (arXiv)

Graph Neural Network (GNN) is an emerging technique for graph-based learning tasks such as node classification. In this work, we reveal the vulnerability of GNN to the imbalance of node labels. Traditional solutions for imbalanced classification (e.g. resampling) are ineffective in node classification without considering the graph structure. Worse still, they may even bring overfitting or underfitting results due to lack of sufficient prior knowledge. To solve these problems, we propose a novel graph neural network framework with curriculum learning (GNN-CL) consisting of two modules. For one thing, we hope to acquire certain reliable interpolation nodes and edges through the novel graph-based oversampling based on smoothness and homophily. For another, we combine graph classification loss and metric learning loss which adjust the distance between different nodes associated with minority class in feature space. Inspired by curriculum learning, we dynamically adjust the weights of different modules during training process to achieve better ability of generalization and discrimination. The proposed framework is evaluated via several widely used graph datasets, showing that our proposed model consistently outperforms the existing state-of-the-art methods.