All in One: Multi-task Prompting for Graph Neural Networks

TL;DR

This paper introduces a multi-task prompting framework for graph neural networks inspired by NLP prompting techniques, aiming to unify and improve performance across diverse graph tasks through reformulation and meta-learning.

Contribution

It proposes a novel multi-task prompting method for graphs, unifying prompt formats, reformulating tasks to graph-level, and applying meta-learning for better initialization, enhancing generalization.

Findings

Outperforms existing methods on multiple graph tasks

Unifies prompt formats for various graph applications

Meta-learning improves prompt initialization and task adaptability

Abstract

Recently, ''pre-training and fine-tuning'' has been adopted as a standard workflow for many graph tasks since it can take general graph knowledge to relieve the lack of graph annotations from each application. However, graph tasks with node level, edge level, and graph level are far diversified, making the pre-training pretext often incompatible with these multiple tasks. This gap may even cause a ''negative transfer'' to the specific application, leading to poor results. Inspired by the prompt learning in natural language processing (NLP), which has presented significant effectiveness in leveraging prior knowledge for various NLP tasks, we study the prompting topic for graphs with the motivation of filling the gap between pre-trained models and various graph tasks. In this paper, we propose a novel multi-task prompting method for graph models. Specifically, we first unify the format of graph prompts and language prompts with the prompt token, token structure, and inserting pattern. In this way, the prompting idea from NLP can be seamlessly introduced to the graph area. Then, to further narrow the gap between various graph tasks and state-of-the-art pre-training strategies, we further study the task space of various graph applications and reformulate downstream problems to the graph-level task. Afterward, we introduce meta-learning to efficiently learn a better initialization for the multi-task prompt of graphs so that our prompting framework can be more reliable and general for different tasks. We conduct extensive experiments, results from which demonstrate the superiority of our method.