no code implementations • 7 Mar 2024 • Wei Ju, Siyu Yi, Yifan Wang, Zhiping Xiao, Zhengyang Mao, Hourun Li, Yiyang Gu, Yifang Qin, Nan Yin, Senzhang Wang, Xinwang Liu, Xiao Luo, Philip S. Yu, Ming Zhang
To tackle these issues, substantial efforts have been devoted to improving the performance of GNN models in practical real-world scenarios, as well as enhancing their reliability and robustness.
no code implementations • 29 Jan 2024 • Wei Ju, Yiyang Gu, Zhengyang Mao, Ziyue Qiao, Yifang Qin, Xiao Luo, Hui Xiong, Ming Zhang
Self-supervised graph representation learning has recently shown considerable promise in a range of fields, including bioinformatics and social networks.
no code implementations • 1 Jan 2024 • Hourun Li, Yusheng Zhao, Zhengyang Mao, Yifang Qin, Zhiping Xiao, Jiaqi Feng, Yiyang Gu, Wei Ju, Xiao Luo, Ming Zhang
However, most of the research in this area is still concentrated on traffic forecasting, while other ITS domains, such as autonomous vehicles and urban planning, still require more attention.
no code implementations • 26 Sep 2023 • Jingyang Yuan, Xiao Luo, Yifang Qin, Zhengyang Mao, Wei Ju, Ming Zhang
Nevertheless, the majority of GNN-based approaches have been examined using well-annotated benchmark datasets, leading to suboptimal performance in real-world graph learning scenarios.
no code implementations • 31 Aug 2023 • Siyu Yi, Zhengyang Mao, Wei Ju, Yongdao Zhou, Luchen Liu, Xiao Luo, Ming Zhang
Graph classification, aiming at learning the graph-level representations for effective class assignments, has received outstanding achievements, which heavily relies on high-quality datasets that have balanced class distribution.
no code implementations • 4 Aug 2023 • Zhengyang Mao, Wei Ju, Yifang Qin, Xiao Luo, Ming Zhang
Recent approaches mainly focus on re-balancing different classes during model training, which fails to explicitly introduce new knowledge and sacrifices the performance of the head classes.