Zobrazeno 1 - 10
of 323
pro vyhledávání: '"Li, Xiaosen"'
Autor:
Zhang, Wentao, Yin, Ziqi, Sheng, Zeang, Li, Yang, Ouyang, Wen, Li, Xiaosen, Tao, Yangyu, Yang, Zhi, Cui, Bin
Publikováno v:
In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022
Graph neural networks (GNNs) have achieved great success in many graph-based applications. However, the enormous size and high sparsity level of graphs hinder their applications under industrial scenarios. Although some scalable GNNs are proposed for
Externí odkaz:
http://arxiv.org/abs/2206.04355
Autor:
Zhang, Wentao, Shen, Yu, Lin, Zheyu, Li, Yang, Li, Xiaosen, Ouyang, Wen, Tao, Yangyu, Yang, Zhi, Cui, Bin
Publikováno v:
The ACM Web Conference 2022
Graph neural networks (GNNs) have achieved state-of-the-art performance in various graph-based tasks. However, as mainstream GNNs are designed based on the neural message passing mechanism, they do not scale well to data size and message passing step
Externí odkaz:
http://arxiv.org/abs/2203.00638
Publikováno v:
In Chemical Engineering Journal 1 November 2024 499
Publikováno v:
In Applied Energy 1 October 2024 371
Autor:
Liao, Huixian, Li, Xiaosen, Qin, Xiao, Wang, Wenji, He, Guodui, Huang, Haojie, Guo, Xu, Chun, Xin, Zhang, Jinyong, Fu, Yunqin, Qin, Zhengyou
Publikováno v:
In Image and Vision Computing September 2024 149
Autor:
Gao, Shicheng, Xu, Jie, Li, Xiaosen, Fu, Fangcheng, Zhang, Wentao, Ouyang, Wen, Tao, Yangyu, Cui, Bin
K-core decomposition is a commonly used metric to analyze graph structure or study the relative importance of nodes in complex graphs. Recent years have seen rapid growth in the scale of the graph, especially in industrial settings. For example, our
Externí odkaz:
http://arxiv.org/abs/2112.14840
Publikováno v:
In Applied Energy 15 August 2024 368
Publikováno v:
In Chemical Engineering Science 5 July 2024 293
Publikováno v:
In Materials Today Communications June 2024 39
Publikováno v:
In Energy 1 June 2024 296