LightGCN: Evaluated and Enhanced
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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.
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.
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5 of 5 references.
References · 5
- 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,139
- 2018Predict then Propagate: Graph Neural Networks meet Personalized PageRankcited by 993
- 2019Neural Graph Collaborative Filteringcited by 661
- 2020Controllable Multi-Interest Framework for Recommendationcited by 93
- 2021A Next Basket Recommendation Reality Checkcited by 15
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