Non-Homophilic Graph Pre-Training and Prompt Learning

Understand

Graphs are ubiquitous for modeling complex relationships between objects across various fields.

• Graph neural networks (GNNs) have become a mainstream technique for graph-based applications, but their performance heavily relies on abundant labeled data.
• To reduce labeling requirement, pre-training and prompt learning has become a popular alternative.
• However, most existing prompt methods do not differentiate homophilic and heterophilic characteristics of real-world graphs.