# GAT-RWOS: Graph Attention-Guided Random Walk Oversampling for Imbalanced Data Classification

arXiv: https://arxiv.org/abs/2412.16394 · HTML: https://mlsanity.com/paper/2412.16394/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 20 Dec 2024.

## Builds on (most cited first, 8 of 8)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks](https://mlsanity.com/paper/2103.08826/) (2021)
- [GraphMixup: Improving Class-Imbalanced Node Classification on Graphs by Self-supervised Context Prediction](https://mlsanity.com/paper/2106.11133/) (2021)
- [Effective Class-Imbalance learning based on SMOTE and Convolutional Neural Networks](https://mlsanity.com/paper/2209.00653/) (2022)
- [UGRWO-Sampling for COVID-19 dataset: A modified random walk under-sampling approach based on graphs to imbalanced data classification](https://mlsanity.com/paper/2002.03521/) (2020)
- [Random Walk-steered Majority Undersampling](https://mlsanity.com/paper/2109.12423/) (2021)

## Built on by (most cited first, 1 of 1)

- [Beyond Imbalance Ratio: Data Characteristics as Critical Moderators of Oversampling Method Selection](https://mlsanity.com/paper/2604.04541/) (2026)

## Abstract (arXiv)

Class imbalance poses a significant challenge in machine learning (ML), often leading to biased models favouring the majority class. In this paper, we propose GAT-RWOS, a novel graph-based oversampling method that combines the strengths of Graph Attention Networks (GATs) and random walk-based oversampling. GAT-RWOS leverages the attention mechanism of GATs to guide the random walk process, focusing on the most informative neighbourhoods for each minority node. By performing attention-guided random walks and interpolating features along the traversed paths, GAT-RWOS generates synthetic minority samples that expand class boundaries while preserving the original data distribution. Extensive experiments on a diverse set of imbalanced datasets demonstrate the effectiveness of GAT-RWOS in improving classification performance, outperforming state-of-the-art oversampling techniques. The proposed method has the potential to significantly improve the performance of ML models on imbalanced datasets and contribute to the development of more reliable classification systems.