# Heterophily-Aware Adaptive Knowledge Distillation for Hypergraph Neural Networks

arXiv: https://arxiv.org/abs/2606.08978 · HTML: https://mlsanity.com/paper/2606.08978/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Graph neural network distillation, one of 12 directions in Knowledge distillation.
- Builds on 11 of its 21 references that are in the map.
- First posted on arXiv 8 Jun 2026.

## Placed in

- Knowledge distillation / [Graph neural network distillation](https://mlsanity.com/distill/graph-neural-network-distillation/)

## Builds on (most cited first, 11 of 11)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Hypergraph Convolution and Hypergraph Attention](https://mlsanity.com/paper/1901.08150/) (2019)
- [UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks](https://mlsanity.com/paper/2105.00956/) (2021)
- [You are AllSet: A Multiset Function Framework for Hypergraph Neural Networks](https://mlsanity.com/paper/2106.13264/) (2021)
- [HNHN: Hypergraph Networks with Hyperedge Neurons](https://mlsanity.com/paper/2006.12278/) (2020)
- [Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods](https://mlsanity.com/paper/2111.04840/) (2021)
- [Self-Supervised Dynamic Hypergraph Recommendation based on Hyper-Relational Knowledge Graph](https://mlsanity.com/paper/2308.07752/) (2023)
- [LightHGNN: Distilling Hypergraph Neural Networks into MLPs for 100× Faster Inference](https://mlsanity.com/paper/2402.04296/) (2024)
- [HyGEN: Regularizing Negative Hyperedge Generation for Accurate Hyperedge Prediction](https://mlsanity.com/paper/2502.05827/) (2025)
- [Enhancing Hyperedge Prediction with Context-Aware Self-Supervised Learning](https://mlsanity.com/paper/2309.05798/) (2023)
- [HC-GST: Heterophily-aware Distribution Consistency based Graph Self-training](https://mlsanity.com/paper/2407.17787/) (2024)

## Abstract (arXiv)

Hypergraph knowledge distillation aims to retain the predictive performance of a hypergraph neural network (HNN) teacher while reducing inference costs through a lightweight student model. In this work, we observe that HNNs exhibit substantially lower prediction performance on heterophilic nodes connected through semantically diverse hyperedges, indicating that the reliability of teacher knowledge varies across nodes. Motivated by this observation, we propose HADES, a heterophily-aware adaptive distillation method for hypergraph neural networks. HADES quantifies node heterophily and leverages it as an estimate of teacher reliability to modulate the transfer of teacher knowledge during distillation. Experimental results on real-world hypergraphs demonstrate that HADES consistently improves student performance across different HNN teachers and distillation objectives. In many cases, the resulting student models surpass the predictive performance of their teachers while achieving up to 12.3 times faster inference.