Authors: Guoxuan Chen, Lianghao Xia, Chao Huang
Abstract: Graph neural networks (GNNs) have demonstrated superior performance in
collaborative recommendation through their ability to conduct high-order
representation smoothing, effectively capturing structural information within
users’ interaction patterns. However, existing GNN paradigms face significant
challenges in scalability and robustness when handling large-scale, noisy, and
real-world datasets. To address these challenges, we present LightGNN, a
lightweight and distillation-based GNN pruning framework designed to
substantially reduce model complexity while preserving essential collaboration
modeling capabilities. Our LightGNN framework introduces a computationally
efficient pruning module that adaptively identifies and removes redundant edges
and embedding entries for model compression. The framework is guided by a
resource-friendly hierarchical knowledge distillation objective, whose
intermediate layer augments the observed graph to maintain performance,
particularly in high-rate compression scenarios. Extensive experiments on
public datasets demonstrate LightGNN’s effectiveness, significantly improving
both computational efficiency and recommendation accuracy. Notably, LightGNN
achieves an 80% reduction in edge count and 90% reduction in embedding entries
while maintaining performance comparable to more complex state-of-the-art
baselines. The implementation of our LightGNN framework is available at the
github repository: https://github.com/HKUDS/LightGNN.
Source: http://arxiv.org/abs/2501.03228v1
