Non-Homophilic Graph Pre-Training and Prompt Learning
This work addresses the challenge of reducing labeling requirements for graph-based applications, particularly for non-homophilic graphs, but it is incremental as it builds on existing pre-training and prompt learning methods.
The paper tackles the problem of graph neural networks' reliance on labeled data by proposing ProNoG, a pre-training and prompt learning framework for non-homophilic graphs, achieving improved performance across ten public datasets.
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. In particular, many real-world graphs are non-homophilic, not strictly or uniformly homophilic with mixing homophilic and heterophilic patterns, exhibiting varying non-homophilic characteristics across graphs and nodes. In this paper, we propose ProNoG, a novel pre-training and prompt learning framework for such non-homophilic graphs. First, we analyze existing graph pre-training methods, providing theoretical insights into the choice of pre-training tasks. Second, recognizing that each node exhibits unique non-homophilic characteristics, we propose a conditional network to characterize the node-specific patterns in downstream tasks. Finally, we thoroughly evaluate and analyze ProNoG through extensive experiments on ten public datasets.