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Found 3 papers, 1 papers with code
Learning from Graphs with Heterophily: Progress and Future
Graphs are structured data that models complex relations between real-world entities.
Prompt Tuning for Multi-View Graph Contrastive Learning
Third, we design a prompting tuning method for our multi-view graph contrastive learning method to bridge the gap between pretexts and downsteam tasks.
Empower Text-Attributed Graphs Learning with Large Language Models (LLMs)
In order to tackle this challenge, we propose a lightweight paradigm called ENG, which adopts a plug-and-play approach to empower text-attributed graphs through node generation using LLMs.
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