解决异构图神经网络预测头瓶颈，提升长尾节点表现
Scalable Heterogeneous Graph Learning via Heterogeneous-aware Orthogonal Prototype Experts
- 用原型路由机制按相似性分配节点到专家
- 在4个真实数据集上均超越SOTA模型，尤其改善尾部节点性能
- 适合关注异构图中长尾分布、追求轻量改造的开发者
异构图神经网络主要依赖更优编码器，但其解码/投影阶段仍采用单一共享线性头，假设其能将丰富节点嵌入映射到标签。我们称之为线性投影瓶颈：在异构图中，上下文多样性与长尾分布使全局头难以捕捉细粒度语义，易过拟合枢纽节点，忽略尾部节点。虽然混合专家（MoE）可缓解此问题，但直接应用会加剧结构失衡并引发专家坍塌。为此，我们提出异构感知正交原型专家框架HOPE，作为标准预测头的即插即用替代方案。HOPE通过可学习的原型路由机制，根据相似性将实例分配给专家，使专家使用符合自然长尾分布，并引入专家正交化以促进多样性、防止坍塌。在四个真实数据集上的实验表明，该方法在多种SOTA HGNN主干网络上均取得一致提升，且开销极小。
原文摘要 · Abstract (English)
Heterogeneous Graph Neural Networks(HGNNs) have advanced mainly through better encoders, yet their decoding/projection stage still relies on a single shared linear head, assuming it can map rich node embeddings to labels. We call this the Linear Projection Bottleneck: in heterogeneous graphs, contextual diversity and long-tail shifts make a global head miss fine semantics, overfit hub nodes, and underserve tail nodes. While Mixture-of-Experts(MoE) could help, naively applying it clashes with structural imbalance and risks expert collapse. We propose a Heterogeneous-aware Orthogonal Prototype Experts framework named HOPE, a plug-and-play replacement for the standard prediction head. HOPE uses learnable prototype-based routing to assign instances to experts by similarity, letting expert usage follow the natural long-tail distribution, and adds expert orthogonalization to encourage diversity and prevent collapse. Experiments on four real datasets show consistent gains across SOTA HGNN backbones with minimal overhead.
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