LightGNN: Simple Graph Neural Network for Recommendation

• URL: http://arxiv.org/abs/2501.03228v3
• Date: Tue, 04 Feb 2025 08:34:08 GMT
• Title: LightGNN: Simple Graph Neural Network for Recommendation
• Authors: Guoxuan Chen, Lianghao Xia, Chao Huang,
• Abstract summary: Graph neural networks (GNNs) have demonstrated superior performance in collaborative recommendation. Existing GNN paradigms face challenges in scalability and robustness when handling large-scale, noisy, and real-world datasets. We present LightGNN, a lightweight and distillation-based GNN pruning framework.
• Score: 14.514770044236375
• License:
• 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.

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