GraphGPT: Graph Instruction Tuning for Large Language Models

Understand

Graph Neural Networks (GNNs) have evolved to understand graph structures through recursive exchanges and aggregations among nodes.

• To enhance robustness, self-supervised learning (SSL) has become a vital tool for data augmentation.
• Traditional methods often depend on fine-tuning with task-specific labels, limiting their effectiveness when labeled data is scarce.
• Our research tackles this by advancing graph model generalization in zero-shot learning environments.