解决图数据少数类识别难题，提升不平衡场景下模型性能。
GraphIFE: Rethinking Graph Imbalance Node Classification via Invariant Learning
- 通过不变特征学习，改善合成节点的质量一致性
- 在多个数据集上显著超越基线模型，尤其提升少数类表现
- 适合处理类别不平衡的图神经网络任务研究者
类别不平衡指数据集中不同类别样本分布不均，少数类样本严重不足。这一问题在图结构数据中同样普遍存在。多数图神经网络（GNN）隐式假设类别分布均衡，因而难以应对不平衡带来的挑战，导致学习偏差，降低少数类性能。本文发现合成节点存在质量不一致问题，影响图不平衡下的模型表现。为此，提出GraphIFE（图不变特征提取）框架，融合图不变学习的两个核心思想，引入策略强化嵌入空间表示，提升模型识别不变特征的能力。大量实验表明，GraphIFE在多个数据集上均优于多种基线方法，具备高效性与强泛化能力。代码已公开于https://github.com/flzeng1/GraphIFE。
原文摘要 · Abstract (English)
The class imbalance problem refers to the disproportionate distribution of samples across different classes within a dataset, where the minority classes are significantly underrepresented. This issue is also prevalent in graph-structured data. Most graph neural networks (GNNs) implicitly assume a balanced class distribution and therefore often fail to account for the challenges introduced by class imbalance, which can lead to biased learning and degraded performance on minority classes. We identify a quality inconsistency problem in synthesized nodes, which leads to suboptimal performance under graph imbalance conditions. To mitigate this issue, we propose GraphIFE (Graph Invariant Feature Extraction), a novel framework designed to mitigate quality inconsistency in synthesized nodes. Our approach incorporates two key concepts from graph invariant learning and introduces strategies to strengthen the embedding space representation, thereby enhancing the model's ability to identify invariant features. Extensive experiments demonstrate the framework's efficiency and robust generalization, as GraphIFE consistently outperforms various baselines across multiple datasets. The code is publicly available at https://github.com/flzeng1/GraphIFE.
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