# LightGCN: Evaluated and Enhanced

arXiv: https://arxiv.org/abs/2312.16183 · HTML: https://mlsanity.com/paper/2312.16183/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 5 references that are in the map.
- First posted on arXiv 17 Dec 2023.

## Builds on (most cited first, 5 of 5)

- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Predict then Propagate: Graph Neural Networks meet Personalized PageRank](https://mlsanity.com/paper/1810.05997/) (2018)
- [Neural Graph Collaborative Filtering](https://mlsanity.com/paper/1905.08108/) (2019)
- [Controllable Multi-Interest Framework for Recommendation](https://mlsanity.com/paper/2005.09347/) (2020)
- [A Next Basket Recommendation Reality Check](https://mlsanity.com/paper/2109.14233/) (2021)

## Abstract (arXiv)

This paper analyses LightGCN in the context of graph recommendation algorithms. Despite the initial design of Graph Convolutional Networks for graph classification, the non-linear operations are not always essential. LightGCN enables linear propagation of embeddings, enhancing performance. We reproduce the original findings, assess LightGCN's robustness on diverse datasets and metrics, and explore Graph Diffusion as an augmentation of signal propagation in LightGCN.