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Frontiers of Computer Science

ISSN 2095-2228

ISSN 2095-2236(Online)

CN 10-1014/TP

Postal Subscription Code 80-970

2018 Impact Factor: 1.129

Front. Comput. Sci.    2024, Vol. 18 Issue (3) : 183343    https://doi.org/10.1007/s11704-024-3803-z
Artificial Intelligence
XGCN: a library for large-scale graph neural network recommendations
Xiran SONG1, Hong HUANG1(), Jianxun LIAN2, Hai JIN1
1. National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
2. Microsoft Research Asia, Beijing 100080, China
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Corresponding Author(s): Hong HUANG   
Just Accepted Date: 09 January 2024   Issue Date: 14 March 2024
 Cite this article:   
Xiran SONG,Hong HUANG,Jianxun LIAN, et al. XGCN: a library for large-scale graph neural network recommendations[J]. Front. Comput. Sci., 2024, 18(3): 183343.
 URL:  
https://academic.hep.com.cn/fcs/EN/10.1007/s11704-024-3803-z
https://academic.hep.com.cn/fcs/EN/Y2024/V18/I3/183343
Fig.1  Overall framework of the XGCN library
Fig.2  XGCN usage example
Category Models
Pure propagation RandNE
Shallow embedding node2vec, UltraGCN
MP or layer-sampling GraphSAGE, GAT, GIN, LightGCN, SimpleX
Decoupling-based PPRGo, SGC, S2GC, SIGN, GAMLP, GBP
Clustering-based Cluster-GCN
Extreme convolution xGCN
Tab.1  Supported models in XGCN
Datasets: (#nodes, #edges) in million
(0.5, 2.9) (1, 9) (2, 27) (3, 49)
Official 85.1 OOM OOM OOM
RecBole 86.1 813 6591 OOM
XGCN 31.6 248 1825 5462
Tab.2  Scalability study of light graph convolution implementations, displayed in seconds for an epoch training
1 X, Liu Y, Liu B, Yin H, Yang Z, Luan D Qian . swSpAMM: optimizing large-scale sparse approximate matrix multiplication on Sunway Taihulight. Frontiers of Computer Science, 2023, 17( 4): 174104
2 W, Zhao S, Mu Y, Hou Z, Lin Y, Chen X, Pan K, Li Y, Lu H, Wang C, Tian Y, Min Z, Feng X, Fan X, Chen P, Wang W, Ji Y, Li X, Wang J R Wen . 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 X, He K, Deng X, Wang Y, Li Y, Zhang M Wang . 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 W, Li M, He Z, Huang X, Wang S, Feng W, Su Y Sun . Graph4Rec: a universal toolkit with graph neural networks for recommender systems. 2023, arXiv preprint arXiv: 2112.01035
5 X, Song J, Lian H, Huang Z, Luo W, Zhou X, Lin M, Wu C, Li X, Xie H Jin . xGCN: an extreme graph convolutional network for large-scale social link prediction. In: Proceedings of the ACM Web Conference 2023. 2023, 349−359
[1] FCS-23803-OF-XS_suppl_1 Download
[2] FCS-23803-OF-XS_suppl_2 Download
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