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arXiv · 2312.16183

LightGCN: Evaluated and Enhanced

Milena Kapralova·Luca Pantea·Andrei Blahovici
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Abstract

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.

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Milena Kapralova, Luca Pantea, Andrei Blahovici. 2023-12-17. LightGCN: Evaluated and Enhanced. https://arxiv.org/abs/2312.16183

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