GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks

Original Paper: https://arxiv.org/abs/2302.08043

By: Zemin Liu, Xingtong Yu, Yuan Fang, Xinming Zhang

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 rely 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-train model in a task-specific manner. Finally, we conduct extensive experiments on five public datasets to evaluate and analyze GraphPrompt.

Summary Notes

Simplifying GraphPrompt - A New Era for Graph Neural Networks

Graph Neural Networks (GNNs) are a powerful tool for analyzing complex structures and relationships in data, used in various fields like social network analysis, molecular structure identification, and webpage classification. Despite their potential, GNNs often require a lot of labeled data to be effective, which can be expensive or difficult to gather.

This has led researchers to pre-train GNNs using self-supervised learning to lessen the need for labeled data. However, applying these pre-trained models to specific tasks has been challenging due to differences in objectives and the absence of a standardized approach.

In this blog, we explore GraphPrompt, a new strategy that aims to make it easier to use pre-trained GNNs for specific tasks, improving their adaptability and efficiency.

The Problems We Face

Using pre-trained GNNs effectively comes down to overcoming two key issues:

• Objective Misalignment: Traditional pre-training methods focus on general graph properties, which might not be helpful for specific tasks, resulting in a poor transfer of knowledge.
• Lack of a Unified Framework: Adapting pre-trained models to specific tasks often requires significant changes, making the process inefficient and difficult to scale.

What is GraphPrompt?

GraphPrompt offers a solution by creating a bridge between the broad capabilities developed during pre-training and the specific needs of downstream tasks. It stands on two main ideas:

• Learnable Prompt: A game-changing feature that introduces task-specific guides to direct the GNN's attention to the most relevant parts of the graph for a given task. These prompts can be fine-tuned, making them even more effective.
• Unified Task Template: GraphPrompt uses a consistent framework for both pre-training and downstream tasks, making it easier to apply the learned knowledge to specific problems without major adjustments.

How Does GraphPrompt Work?

GraphPrompt's strategy includes:

• Subgraph Utilization: It represents tasks at both the node and graph level using subgraphs, allowing the framework to handle a diverse range of applications.
• Task-Specific Prompts: These prompts adjust the GNN's output for the task at hand without changing the model's fundamental structure.
• Experimental Validation: Tested on five public datasets, GraphPrompt proved more effective in node and graph classification tasks than traditional methods, showcasing its strength and versatility.

Achievements and Outcomes

GraphPrompt introduces several advancements:

• A Unified Framework: It simplifies using pre-trained GNNs for various tasks, increasing the models' versatility and utility.
• Learnable Prompts: This innovation helps in better utilizing pre-trained knowledge by focusing on the important features of the graph for the task.
• Proven Effectiveness: With superior performance across different datasets and tasks, GraphPrompt's benefits are clear.

In practice, GraphPrompt outshines traditional pre-training and fine-tuning methods, showing its robustness and versatility across different tasks.

Looking Ahead

GraphPrompt marks a significant leap in using pre-trained GNNs, offering a more flexible and efficient way to bridge the gap between pre-training and real-world applications.

By addressing objective misalignment and providing a standardized framework, it opens new avenues for using GNNs, especially where labeled data is scarce.

Future work could explore further optimizations in prompt design and extending GraphPrompt to even more complex tasks. Its adaptability and scalability suggest it could have wide applications in various domains requiring advanced graph analysis.

In conclusion, GraphPrompt is a promising advancement for enhancing GNNs' applicability and efficiency in solving real-world problems.

For AI engineers in enterprises, incorporating GraphPrompt into their workflows could lead to better results in areas ranging from analyzing social networks to discovering new drugs, making it a valuable tool for advancing the field.