GraphPrompts

Overview

Liu et al., 2023 introduces GraphPrompt, a new prompting framework for graphs to improve performance on downstream tasks.

Key Concepts

GraphPrompt is designed to leverage the structural information inherent in graph data to enhance language model performance on graph-related tasks.

Applications

• Graph Classification: Categorizing different types of graphs
• Node Classification: Labeling individual nodes in a graph
• Link Prediction: Predicting missing connections between nodes
• Graph Generation: Creating new graph structures
• Graph-to-Text: Converting graph representations to natural language

Benefits

• Structural Awareness: Incorporates graph topology information
• Improved Performance: Better results on graph-related tasks
• Flexible Framework: Adaptable to various graph types and tasks
• Efficient Processing: Optimized for handling graph-structured data

Current Status

More coming soon!

Related Topics

• Chain-of-Thought Prompting - Understanding reasoning techniques
• Few-Shot Prompting - Learning from examples
• Prompt Engineering Guide - General prompt engineering techniques

References

• Liu et al., (2023) - GraphPrompt: Unifying Pre-training and Downstream Tasks for Graph Neural Networks