4 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…
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…
Introducing Expertise Logic into Graph Representation Learning from A Causal Perspective
Hang Gao, Jiangmeng Li, Wenwen Qiang +5
Benefiting from the injection of human prior knowledge, graphs, as derived discrete data, are semantically dense so that models can efficiently learn the semantic information from…