# SVD-GCN: A Simplified Graph Convolution Paradigm for Recommendation

arXiv: https://arxiv.org/abs/2208.12689 · HTML: https://mlsanity.com/paper/2208.12689/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2022.

## Built on by (most cited first, 16 of 16)

- [Blurring-Sharpening Process Models for Collaborative Filtering](https://mlsanity.com/paper/2211.09324/) (2022)
- [On Manipulating Signals of User-Item Graph: A Jacobi Polynomial-based Graph Collaborative Filtering](https://mlsanity.com/paper/2306.03624/) (2023)
- [ADEdgeDrop: Adversarial Edge Dropping for Robust Graph Neural Networks](https://mlsanity.com/paper/2403.09171/) (2024)
- [A Survey on Recommendation Unlearning: Fundamentals, Taxonomy, Evaluation, and Open Questions](https://mlsanity.com/paper/2412.12836/) (2024)
- [Are We Measuring Oversmoothing in Graph Neural Networks Correctly?](https://mlsanity.com/paper/2502.04591/) (2025)
- [How Does Message Passing Improve Collaborative Filtering?](https://mlsanity.com/paper/2404.08660/) (2024)
- [SVD-AE: Simple Autoencoders for Collaborative Filtering](https://mlsanity.com/paper/2405.04746/) (2024)
- [Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysis](https://mlsanity.com/paper/2308.00404/) (2023)
- [Stuart-Landau Oscillatory Graph Neural Network](https://mlsanity.com/paper/2511.08094/) (2025)
- [Dimension Independent Mixup for Hard Negative Sample in Collaborative Filtering](https://mlsanity.com/paper/2306.15905/) (2023)
- [Fair Augmentation for Graph Collaborative Filtering](https://mlsanity.com/paper/2408.12208/) (2024)
- [MADE: Graph Backdoor Defense with Masked Unlearning](https://mlsanity.com/paper/2411.18648/) (2024)
- [Space-Invariant Projection in Streaming Network Embedding](https://mlsanity.com/paper/2303.06293/) (2023)
- [Explainable Recommender with Geometric Information Bottleneck](https://mlsanity.com/paper/2305.05331/) (2023)
- [Lighter-X: An Efficient and Plug-and-play Strategy for Graph-based Recommendation through Decoupled Propagation](https://mlsanity.com/paper/2510.10105/) (2025)
- [Debiasing Message Passing to Mitigate Popularity Bias in GNN-based Collaborative Filtering](https://mlsanity.com/paper/2605.11145/) (2026)

## Abstract (arXiv)

With the tremendous success of Graph Convolutional Networks (GCNs), they have been widely applied to recommender systems and have shown promising performance. However, most GCN-based methods rigorously stick to a common GCN learning paradigm and suffer from two limitations: (1) the limited scalability due to the high computational cost and slow training convergence; (2) the notorious over-smoothing issue which reduces performance as stacking graph convolution layers. We argue that the above limitations are due to the lack of a deep understanding of GCN-based methods. To this end, we first investigate what design makes GCN effective for recommendation. By simplifying LightGCN, we show the close connection between GCN-based and low-rank methods such as Singular Value Decomposition (SVD) and Matrix Factorization (MF), where stacking graph convolution layers is to learn a low-rank representation by emphasizing (suppressing) components with larger (smaller) singular values. Based on this observation, we replace the core design of GCN-based methods with a flexible truncated SVD and propose a simplified GCN learning paradigm dubbed SVD-GCN, which only exploits K-largest singular vectors for recommendation. To alleviate the over-smoothing issue, we propose a renormalization trick to adjust the singular value gap, resulting in significant improvement. Extensive experiments on three real-world datasets show that our proposed SVD-GCN not only significantly outperforms state-of-the-arts but also achieves over 100x and 10x speedups over LightGCN and MF, respectively.