Non-Homophilic Graph Pre-Training and Prompt Learning

• URL: http://arxiv.org/abs/2408.12594v3
• Date: Fri, 30 Aug 2024 10:55:58 GMT
• Title: Non-Homophilic Graph Pre-Training and Prompt Learning
• Authors: Xingtong Yu, Jie Zhang, Yuan Fang, Renhe Jiang,
• Abstract summary: We propose ProNoG, a novel pre-training and prompt learning framework for 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.
• Score: 11.996173149569627
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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