LightGCN: Evaluated and Enhanced

• URL: http://arxiv.org/abs/2312.16183v1
• Date: Sun, 17 Dec 2023 15:18:18 GMT
• Title: LightGCN: Evaluated and Enhanced
• Authors: Milena Kapralova, Luca Pantea and Andrei Blahovici
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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