Abstract Graph Neural Networks (GNNs) have shown satisfying performance on various graph learning tasks. To achieve better fitting capability, most GNNs are with a large number of parameters, which makes these GNNs computationally expensive. Therefore, it
Abstract Graph Neural Networks (GNNs) have shown satisfying performance on various graph learning tasks. To achieve better fitting capability, most GNNs are with a large number of parameters, which makes these GNNs computationally expensive. Therefore, it
复制标题
摘要图神经网络（GNN）在各种图学习任务上表现出了令人满意的性能。
DOI：
--
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Li, Jundong
中科院分区：
文献类型：
--
作者：
Dong, Yushun;Zhang, Binichi;Yuan, Yiling;Zou, Na;Wang, Qi;Li, Jundong
