Rapid advancement in machine learning is increasing the demand for effective graph data analysis. However, real-world graph data often exhibits class imbalance, leading to poor performance of standard machine learning models on underrepresented classes. To address this,Class-ImbalancedLearning onGraphs (CILG) has emerged as a promising solution that combines graph representation learning and class-imbalanced learning. This survey provides a comprehensive understanding of CILG’s current state-of-the-art, establishing the first systematic taxonomy of existing work and its connections to traditional imbalanced learning. We critically analyze recent advances and discuss key open problems. A continuously updated reading list of relevant articles and code implementations is available athttps://github.com/yihongma/CILG-Papers.
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Decomposition of class II graphs into two class I graphs
- PAR ID:
- 10498036
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Discrete Mathematics
- Volume:
- 346
- Issue:
- 12
- ISSN:
- 0012-365X
- Page Range / eLocation ID:
- 113610
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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