Scalable Heterogeneous Graph Learning via Heterogeneous-aware Orthogonal Prototype Experts
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866911362978938880 |
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| author | Zhou, Wei Huang, Hong Shi, Ruize Liu, Bang |
| author_facet | Zhou, Wei Huang, Hong Shi, Ruize Liu, Bang |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_05537 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Scalable Heterogeneous Graph Learning via Heterogeneous-aware Orthogonal Prototype Experts Zhou, Wei Huang, Hong Shi, Ruize Liu, Bang Machine Learning Artificial Intelligence 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. |
| title | Scalable Heterogeneous Graph Learning via Heterogeneous-aware Orthogonal Prototype Experts |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2601.05537 |