no code implementations • 29 Sep 2023 • Yiqiao Li, Jianlong Zhou, Yifei Dong, Niusha Shafiabady, Fang Chen
Graph neural networks (GNNs) have proven their efficacy in a variety of real-world applications, but their underlying mechanisms remain a mystery.
no code implementations • 3 Jan 2023 • Boyuan Zheng, Jianlong Zhou, Chunjie Liu, Yiqiao Li, Fang Chen
As one of the prevalent methods to achieve automation systems, Imitation Learning (IL) presents a promising performance in a wide range of domains.
no code implementations • 30 Dec 2022 • Yiqiao Li, Jianlong Zhou, Boyuan Zheng, Fang Chen
With the rapid deployment of graph neural networks (GNNs) based techniques into a wide range of applications such as link prediction, node classification, and graph classification the explainability of GNNs has become an indispensable component for predictive and trustworthy decision-making.
no code implementations • 26 Jul 2022 • Yiqiao Li, Jianlong Zhou, Sunny Verma, Fang Chen
Graph neural networks (GNNs) have demonstrated a significant boost in prediction performance on graph data.