Prompt Tuning for Multi-View Graph Contrastive Learning
Chenghua Gong Xiang Li Jianxiang Yu Cheng Yao Jiaqi Tan Chengcheng Yu Dawei Yin
In recent years, "pre-training and fine-tuning" has emerged as a promising
approach in addressing the issues of label dependency and poor generalization
performance in traditional GNNs. To reduce labeling requirement, the
"pre-train, fine-tune" and "pre-train, prompt" paradigms have become
increasingly common. In particular, prompt tuning is a popular alternative to
"pre-training and fine-tuning" in natural language processing, which is
designed to narrow the gap between pre-training and downstream objectives.
However, existing study of prompting on graphs is still limited, lacking a
framework that can accommodate commonly used graph pre-training methods and
downstream tasks. In this paper, we propose a multi-view graph contrastive
learning method as pretext and design a prompting tuning for it. Specifically,
we first reformulate graph pre-training and downstream tasks into a common
format. Second, we construct multi-view contrasts to capture relevant
information of graphs by GNN. Third, we design a prompting tuning method for
our multi-view graph contrastive learning method to bridge the gap between
pretexts and downsteam tasks. Finally, we conduct extensive experiments on
benchmark datasets to evaluate and analyze our proposed method.