aiqbench analysis
This paper introduces HADES, a method that adapts knowledge distillation in hypergraph neural networks by accounting for node heterophily to better estimate teacher reliability. By modulating knowledge transfer based on these reliability estimates, the approach consistently improves student model performance and achieves up to 12.3x faster inference speeds. The technique offers a practical pathway for deploying efficient student models that outperform their larger teachers, particularly in complex, semantically diverse graph structures.
Heterophily-Aware Adaptive Knowledge Distillation for Hypergraph Neural Networks
arXiv:2606.08978v1 Announce Type: new Abstract: 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.
Abstract: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.
Machine Learning (cs.LG)
arXiv:2606.08978 [cs.LG]
(or arXiv:2606.08978v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.08978 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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