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.

翻译：图作为一种普遍存在的结构，广泛应用于各个领域中对对象间复杂关系的建模。图神经网络已成为基于图的应用的主流技术，但其性能严重依赖于大量标注数据。为了降低标注需求，预训练与提示学习已成为一种流行的替代方案。然而，现有的大多数提示方法并未区分现实世界图的同配性与异配性特征。具体而言，许多现实世界图是非齐次的，并非严格或均匀的同配性，而是混合了同配与异配模式，在不同图及节点间表现出变化的非齐次特性。本文提出ProNoG，一种针对此类非齐次图的新型预训练与提示学习框架。首先，我们分析了现有的图预训练方法，为预训练任务的选择提供了理论见解。其次，认识到每个节点都表现出独特的非齐次特性，我们提出了一种条件网络来刻画下游任务中节点特定的模式。最后，我们在十个公共数据集上通过大量实验对ProNoG进行了全面评估与分析。