# One Router to Route Them All: Homogeneous Expert Routing for Heterogeneous Graph Transformers

arXiv: https://arxiv.org/abs/2511.07603 · HTML: https://mlsanity.com/paper/2511.07603/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in MoE for time series and graphs, one of 12 directions in Mixture-of-experts.
- Builds on 7 of its 18 references that are in the map.
- First posted on arXiv 10 Nov 2025.

## Placed in

- Mixture-of-experts / [MoE for time series and graphs](https://mlsanity.com/moe/moe-for-time-series-and-graphs/)

## Builds on (most cited first, 7 of 7)

- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://mlsanity.com/paper/2101.03961/) (2021)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [GLaM: Efficient Scaling of Language Models with Mixture-of-Experts](https://mlsanity.com/paper/2112.06905/) (2021)
- [Graph Neural Networks for Social Recommendation](https://mlsanity.com/paper/1902.07243/) (2019)
- [Heterogeneous Graph Transformer](https://mlsanity.com/paper/2003.01332/) (2020)
- [Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks](https://mlsanity.com/paper/2101.05974/) (2021)

## Abstract (arXiv)

A common practice in heterogeneous graph neural networks (HGNNs) is to condition parameters on node/edge types, assuming types reflect semantic roles. However, this can cause overreliance on surface-level labels and impede cross-type knowledge transfer. We explore integrating Mixture-of-Experts (MoE) into HGNNs--a direction underexplored despite MoE's success in homogeneous settings. Crucially, we question the need for type-specific experts. We propose Homogeneous Expert Routing (HER), an MoE layer for Heterogeneous Graph Transformers (HGT) that stochastically masks type embeddings during routing to encourage type-agnostic specialization. Evaluated on IMDB, ACM, and DBLP for link prediction, HER consistently outperforms standard HGT and a type-separated MoE baseline. Analysis on IMDB shows HER experts specialize by semantic patterns (e.g., movie genres) rather than node types, confirming routing is driven by latent semantics. Our work demonstrates that regularizing type dependence in expert routing yields more generalizable, efficient, and interpretable representations--a new design principle for heterogeneous graph learning.