Conclusion
This work introduces a GNN library, XGCN, which is designed to assist users in rapidly developing and running large-scale GNN recommendation models. We offer highly scalable GNN reproductions and include a recently proposed GNN model: xGCN. Experimental evaluations on datasets of varying scales demonstrate the superior scalability of our XGCN library.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant Nos. 62172174, 61932004).
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Song, X., Huang, H., Lian, J. et al. XGCN: a library for large-scale graph neural network recommendations. Front. Comput. Sci. 18, 183343 (2024). https://doi.org/10.1007/s11704-024-3803-z
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DOI: https://doi.org/10.1007/s11704-024-3803-z