4 citations · 4 across the 4 of their papers we have counts for
4 papers
Heterogeneous Attributed Graph Learning via Neighborhood-Aware Star Kernels
Hong Huang, Chengyu Yao, Haiming Chen +1
Attributed graphs, typically characterized by irregular topologies and a mix of numerical and categorical attributes, are ubiquitous in diverse domains such as social networks, bio…
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…
Rethinking Causal Relationships Learning in Graph Neural Networks
Hang Gao, Chengyu Yao, Jiangmeng Li +5
Graph Neural Networks (GNNs) demonstrate their significance by effectively modeling complex interrelationships within graph-structured data. To enhance the credibility and robustne…