Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

5-2023

Abstract

Graphs can model complex relationships between objects, enabling a myriad of Web applications such as online page/article classification and social recommendation. While graph neural networks (GNNs) have emerged as a powerful tool for graph representation learning, in an end-to-end supervised setting, their performance heavily relies on a large amount of task-specific supervision. To reduce labeling requirement, the "pre-train, fine-tune"and "pre-train, prompt"paradigms have become increasingly common. In particular, prompting is a popular alternative to fine-tuning in natural language processing, which is designed to narrow the gap between pre-training and downstream objectives in a task-specific manner. However, existing study of prompting on graphs is still limited, lacking a universal treatment to appeal to different downstream tasks. In this paper, we propose GraphPrompt, a novel pre-training and prompting framework on graphs. GraphPrompt not only unifies pre-training and downstream tasks into a common task template, but also employs a learnable prompt to assist a downstream task in locating the most relevant knowledge from the pre-trained model in a task-specific manner. Finally, we conduct extensive experiments on five public datasets to evaluate and analyze GraphPrompt.

Keywords

Graph neural networks, pre-training, prompt, few-shot learning

Discipline

Information Security

Research Areas

Data Science and Engineering

Publication

Proceedings of the 2023 ACM Web Conference, Austin, USA, April 30-May 4

First Page

417

Last Page

428

Identifier

10.1145/3543507.3583386

Publisher

ACM

City or Country

New York

Additional URL

http://doi.org/10.1145/3543507.3583386

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