4 citations · 5 across the 9 of their papers we have counts for
5 papers · 1 filter
LLM Enhancers for GNNs: An Analysis from the Perspective of Causal Mechanism Identification
Hang Gao, Wenxuan Huang, Fengge Wu +3
The use of large language models (LLMs) as feature enhancers to optimize node representations, which are then used as inputs for graph neural networks (GNNs), has shown significant…
Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Representation Learning
Hang Gao, Chenhao Zhang, Tie Wang +4
Large Language Models (LLMs) have achieved remarkable success across various domains. However, they still face significant challenges, including high computational costs for traini…
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…
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…