Non-Homophilic Graph Pre-Training and Prompt Learning
<|reference_start|>Non-Homophilic Graph Pre-Training and Prompt Learning: 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.<|reference_end|>
@article{yu2024non-homophilic,
    title={Non-Homophilic Graph Pre-Training and Prompt Learning},
    author={Xingtong Yu, Jie Zhang, Yuan Fang, Renhe Jiang},
    journal={arXiv preprint arXiv:2408.12594},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.12594},
    primaryClass={cs.LG}
}