27 citations · 32 across the 11 of their papers we have counts for
4 papers · 1 filter
Bootstrapping Heterogeneous Graph Representation Learning via Large Language Models: A Generalized Approach
Hang Gao, Chenhao Zhang, Fengge Wu +3
Graph representation learning methods are highly effective in handling complex non-Euclidean data by capturing intricate relationships and features within graph structures. However…
MBDS: A Multi-Body Dynamics Simulation Dataset for Graph Networks Simulators
Sheng Yang, Fengge Wu, Junsuo Zhao
Modeling the structure and events of the physical world constitutes a fundamental objective of neural networks. Among the diverse approaches, Graph Network Simulators (GNS) have em…
Molecular Graph Representation Learning via Structural Similarity Information
Chengyu Yao, Hong Huang, Hang Gao +3
Graph Neural Networks (GNNs) have been widely employed for feature representation learning in molecular graphs. Therefore, it is crucial to enhance the expressiveness of feature re…
Graph Partial Label Learning with Potential Cause Discovering
Hang Gao, Jiaguo Yuan, Jiangmeng Li +4
Graph Neural Networks (GNNs) have garnered widespread attention for their potential to address the challenges posed by graph representation learning, which face complex graph-struc…