no code implementations • 2 Mar 2024 • Wei Ju, Yusheng Zhao, Yifang Qin, Siyu Yi, Jingyang Yuan, Zhiping Xiao, Xiao Luo, Xiting Yan, Ming Zhang
Toward this end, this paper proposes Conjoint Spatio-Temporal graph neural network (abbreviated as COOL), which models heterogeneous graphs from prior and posterior information to conjointly capture high-order spatio-temporal relationships.
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 • 14 Jun 2023 • Jingyang Yuan, Xiao Luo, Yifang Qin, Yusheng Zhao, Wei Ju, Ming Zhang
Since this regularization term cannot utilize label information, it can enhance the robustness of node representations to label noise.
no code implementations • 11 Apr 2023 • Wei Ju, Zheng Fang, Yiyang Gu, Zequn Liu, Qingqing Long, Ziyue Qiao, Yifang Qin, Jianhao Shen, Fang Sun, Zhiping Xiao, Junwei Yang, Jingyang Yuan, Yusheng Zhao, Yifan Wang, Xiao Luo, Ming Zhang
Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been widely studied in a range of fields, including machine learning and data mining.