针对图节点分类中类别差异大的问题，提出融合专家模型的新方法。
Graph Attention Specialized Expert Fusion Model for Node Classification: Based on Cora and Pubmed Datasets
- 为不同类别训练专用模型，用Wasserstein-Rubinstein距离优化融合过程。
- 类别2准确率提升至79.9%，整体波动系数降低77.6%。
- 适合处理类别不平衡的图数据分类任务，代码已开源。
图节点分类是图神经网络的基础任务，旨在为节点分配预定义标签。在PubMed引用网络数据集上，我们观察到类别间分类难度差异显著：类别2在传统GCN下仅达74.4%准确率，比类别1低7.5%。为此，我们提出增强型专家融合模型（WR-EFM），为类别0/1训练带层归一化与残差连接的GNN模型，为类别2采用多跳图注意力网络（GAT）。Wasserstein-Rubinstein（WR）距离度量优化模型表示相似性，尤其提升类别2性能。自适应融合策略基于类别表现动态加权，类别2的GAT权重设为0.8。WR距离还通过测量模型表示分布差异，指导更合理的特征融合。实验表明，WR-EFM在各类别上实现均衡性能：类别0达77.8%，类别1达78.0%，类别2达79.9%，优于单一模型与标准融合方法。其类别准确率变异系数（CV）为0.013，较GCN的0.058下降77.6%，体现更强稳定性。尤其类别2准确率相较GCN提升5.5%，验证了WR引导融合在捕捉复杂结构模式上的有效性。该工作为处理类别不平衡图分类提供新范式。项目代码已发布于https://github.com/s010m00n/GASEM4NC。
原文摘要 · Abstract (English)
Graph node classification is a fundamental task in graph neural networks (GNNs), aiming to assign predefined class labels to nodes. On the PubMed citation network dataset, we observe significant classification difficulty disparities, with Category 2 achieving only 74.4% accuracy in traditional GCN, 7.5% lower than Category 1. To address this, we propose a Wasserstein-Rubinstein (WR) distance enhanced Expert Fusion Model (WR-EFM), training specialized GNN models for Categories 0/1 (with layer normalization and residual connections) and Multi-hop Graph Attention Networks (GAT) for Category 2. The WR distance metric optimizes representation similarity between models, particularly focusing on improving Category 2 performance. Our adaptive fusion strategy dynamically weights models based on category-specific performance, with Category 2 assigned a GAT weight of 0.8. WR distance further guides the fusion process by measuring distributional differences between model representations, enabling more principled integration of complementary features. Experimental results show WR-EFM achieves balanced accuracy across categories: 77.8% (Category 0), 78.0% (Category 1), and 79.9% (Category 2), outperforming both single models and standard fusion approaches. The coefficient of variation (CV) of WR-EFM's category accuracies is 0.013, 77.6% lower than GCN's 0.058, demonstrating superior stability. Notably, WR-EFM improves Category 2 accuracy by 5.5% compared to GCN, verifying the effectiveness of WR-guided fusion in capturing complex structural patterns. This work provides a novel paradigm for handling class-imbalanced graph classification tasks. To promote the research community, we release our project at https://github.com/s010m00n/GASEM4NC.
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