# Geometric Imbalance in Semi-Supervised Node Classification

arXiv: https://arxiv.org/abs/2303.10371 · HTML: https://mlsanity.com/paper/2303.10371/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2023.

## Built on by (most cited first, 5 of 5)

- [Rethinking Semi-Supervised Imbalanced Node Classification from Bias-Variance Decomposition](https://mlsanity.com/paper/2310.18765/) (2023)
- [IceBerg: Debiased Self-Training for Class-Imbalanced Node Classification](https://mlsanity.com/paper/2502.06280/) (2025)
- [Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active Learning](https://mlsanity.com/paper/2308.08823/) (2023)
- [Overcoming Class Imbalance: Unified GNN Learning with Structural and Semantic Connectivity Representations](https://mlsanity.com/paper/2412.20656/) (2024)
- [UPL: Uncertainty-aware Pseudo-labeling for Imbalance Transductive Node Classification](https://mlsanity.com/paper/2502.00716/) (2025)

## Abstract (arXiv)

Class imbalance in graph data presents a significant challenge for effective node classification, particularly in semi-supervised scenarios. In this work, we formally introduce the concept of geometric imbalance, which captures how message passing on class-imbalanced graphs leads to geometric ambiguity among minority-class nodes in the riemannian manifold embedding space. We provide a rigorous theoretical analysis of geometric imbalance on the riemannian manifold and propose a unified framework that explicitly mitigates it through pseudo-label alignment, node reordering, and ambiguity filtering. Extensive experiments on diverse benchmarks show that our approach consistently outperforms existing methods, especially under severe class imbalance. Our findings offer new theoretical insights and practical tools for robust semi-supervised node classification.