XGCN: a library for large-scale graph neural network recommendations

Xiran SONG; Hong HUANG; Jianxun LIAN; Hai JIN

doi:10.1007/s11704-024-3803-z

Front. Comput. Sci. ›› 2024, Vol. 18 ›› Issue (3) : 183343 DOI: 10.1007/s11704-024-3803-z

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XGCN: a library for large-scale graph neural network recommendations

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Xiran SONG, Hong HUANG, Jianxun LIAN, Hai JIN. XGCN: a library for large-scale graph neural network recommendations. Front. Comput. Sci., 2024, 18(3): 183343 DOI:10.1007/s11704-024-3803-z

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References

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[1]	Liu X, Liu Y, Yin B, Yang H, Luan Z, Qian D . swSpAMM: optimizing large-scale sparse approximate matrix multiplication on Sunway Taihulight. Frontiers of Computer Science, 2023, 17( 4): 174104

[2]

Zhao W, Mu S, Hou Y, Lin Z, Chen Y, Pan X, Li K, Lu Y, Wang H, Tian C, Min Y, Feng Z, Fan X, Chen X, Wang P, Ji W, Li Y, Wang X, Wen J R. RecBole: towards a unified, comprehensive and efficient framework for recommendation algorithms. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 2021, 4653−4664

[3]	He X, Deng K, Wang X, Li Y, Zhang Y, Wang M. LightGCN: simplifying and powering graph convolution network for recommendation. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2020, 639−648

[4]	Li W, He M, Huang Z, Wang X, Feng S, Su W, Sun Y. Graph4Rec: a universal toolkit with graph neural networks for recommender systems. 2023, arXiv preprint arXiv: 2112.01035

[5]	Song X, Lian J, Huang H, Luo Z, Zhou W, Lin X, Wu M, Li C, Xie X, Jin H. xGCN: an extreme graph convolutional network for large-scale social link prediction. In: Proceedings of the ACM Web Conference 2023. 2023, 349−359