AAAI 2026technical0 citations
Pareto-Based Heterogeneous Knowledge Distillation for MLPs on Graphs
Wenrui Zhao, Yijun Tian, Zhichao Xu, Yawei Wang, Chuxu Zhang
Abstract
Heterogeneous Graph Neural Networks (HGNNs) have demonstrated remarkable capabilities in capturing effective information in heterogeneous graphs, achieving outstanding performance in various learning tasks. However, the heavy dependency of HGNNs on neighbors information may result in high latency, which restricts their practicality in real-world applications. Recent studies have attempted to overcome such latency in Graph Neural Networks (GNNs) by distilling knowledge into student models that do not rely on graph structure. But these approaches primarily focus on replicating teachers
BibTeX
@inproceedings{aaai2026_paretobasedheter,
title = {Pareto-Based Heterogeneous Knowledge Distillation for MLPs on Graphs},
author = {Wenrui Zhao and Yijun Tian and Zhichao Xu and Yawei Wang and Chuxu Zhang},
booktitle = {AAAI 2026},
year = {2026}
}