no code implementations • 24 May 2024 • Jialin Zhao, Yingtao Zhang, Xinghang Li, Huaping Liu, Carlo Vittorio Cannistraci
The growing computational demands posed by increasingly number of neural network's parameters necessitate low-memory-consumption training approaches.
no code implementations • NeurIPS 2021 • Jialin Zhao, Yuxiao Dong, Ming Ding, Evgeny Kharlamov, Jie Tang
Notably, message passing based GNNs, e. g., graph convolutional networks, leverage the immediate neighbors of each node during the aggregation process, and recently, graph diffusion convolution (GDC) is proposed to expand the propagation neighborhood by leveraging generalized graph diffusion.
1 code implementation • 1 Jan 2021 • Jialin Zhao, Yuxiao Dong, Jie Tang, Ming Ding, Kuansan Wang
Graph convolutional networks (GCNs) have emerged as a powerful framework for mining and learning with graphs.
