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Found 3 papers, 2 papers with code
Advancing Graph Representation Learning with Large Language Models: A Comprehensive Survey of Techniques
This collaboration harnesses the sophisticated linguistic capabilities of LLMs to improve the contextual understanding and adaptability of graph models, thereby broadening the scope and potential of GRL.
ULTRA-DP: Unifying Graph Pre-training with Multi-task Graph Dual Prompt
Based on this framework, we propose a prompt-based transferability test to find the most relevant pretext task in order to reduce the semantic gap.
HINormer: Representation Learning On Heterogeneous Information Networks with Graph Transformer
To bridge this gap, in this paper we investigate the representation learning on HINs with Graph Transformer, and propose a novel model named HINormer, which capitalizes on a larger-range aggregation mechanism for node representation learning.
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