针对图数据分类不平衡问题，提出聚类感知的节点生成与分簇更新方法。
ECGN: A Cluster-Aware Approach to Graph Neural Networks for Imbalanced Classification
- 按聚类分组学习不同聚合方式，提升模型对局部结构的适应性
- 通过生成少数类节点，清晰化类别间决策边界，性能提升最高达11%
- 可兼容任意GNN和聚类方法，适用于真实图数据中的不平衡场景
图节点分类是常见任务，理想分类器需应对类别分布不均衡问题，并利用真实图数据中的聚类结构。现有图神经网络（GNN）未同时解决这两方面挑战。本文提出增强型聚类感知图网络（ECGN），通过引入聚类特定训练与合成节点生成来解决该问题。不同于传统GNN对所有节点使用统一更新机制，ECGN为不同聚类学习不同的信息聚合方式，并利用聚类结构生成少数类节点，以增强类别间决策边界的清晰度。结合聚类感知嵌入与全局融合步骤，显著提升节点表示质量。该方法可与任意基础GNN及聚类算法协同工作。实验表明，在多个常用基准数据集上，ECGN性能优于最接近的对比方法，最高提升达11%。
原文摘要 · Abstract (English)
Classifying nodes in a graph is a common problem. The ideal classifier must adapt to any imbalances in the class distribution. It must also use information in the clustering structure of real-world graphs. Existing Graph Neural Networks (GNNs) have not addressed both problems together. We propose the Enhanced Cluster-aware Graph Network (ECGN), a novel method that addresses these issues by integrating cluster-specific training with synthetic node generation. Unlike traditional GNNs that apply the same node update process for all nodes, ECGN learns different aggregations for different clusters. We also use the clusters to generate new minority-class nodes in a way that helps clarify the inter-class decision boundary. By combining cluster-aware embeddings with a global integration step, ECGN enhances the quality of the resulting node embeddings. Our method works with any underlying GNN and any cluster generation technique. Experimental results show that ECGN consistently outperforms its closest competitors by up to 11% on some widely studied benchmark datasets.
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