no code implementations • 21 Feb 2024 • Yufei He, Bryan Hooi
Graph learning has predominantly focused on single-graph models, tailored to specific tasks or datasets, lacking the ability to transfer learned knowledge to different domains.
no code implementations • 28 Jun 2023 • Wei Wang, Kaigui Xie, Hongbin Wang, Xingzhe Hou, Tao Chen, Hongzhou Chen, Yufei He
In this paper, we focus on the interdependent electric power and natural gas distribution systems (IENDS) and propose a comprehensive "supply - demand - repair" strategy to help the IENDS tide over the emergency periods after disasters by coordinating various emergency resources.
2 code implementations • 10 Apr 2023 • Zhenyu Hou, Yufei He, Yukuo Cen, Xiao Liu, Yuxiao Dong, Evgeny Kharlamov, Jie Tang
Graph self-supervised learning (SSL), including contrastive and generative approaches, offers great potential to address the fundamental challenge of label scarcity in real-world graph data.
4 code implementations • 8 Dec 2021 • Chenhui Zhang, Yufei He, Yukuo Cen, Zhenyu Hou, Wenzheng Feng, Yuxiao Dong, Xu Cheng, Hongyun Cai, Feng He, Jie Tang
However, it is unclear how to best design the generalization strategies in GNNs, as it works in a semi-supervised setting for graph data.
Ranked #3 on Node Property Prediction on ogbn-papers100M
no code implementations • 26 Feb 2021 • Wei Wang, Yufei He, Xiaofu Xiong, Hongzhou Chen
Extreme circumstances in which a local distribution system is electrically isolated from the main power supply may not always be avoidable.
no code implementations • 6 Dec 2020 • Wei Wang, Xiaofu Xiong, Yufei He, Jian Hu, Hongzhou Chen
Mobile energy resources (MERs) have been shown to boost DS resilience effectively in recent years.