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LightGCN: Evaluated and Enhanced
December 17, 2023 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
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Authors
Milena Kapralova, Luca Pantea, Andrei Blahovici
arXiv ID
2312.16183
Category
cs.IR: Information Retrieval
Cross-listed
cs.LG
Citations
1
Venue
arXiv.org
Last Checked
4 months ago
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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