RAGraph: A General Retrieval-Augmented Graph Learning Framework (2410.23855v2)

Abstract: Graph Neural Networks (GNNs) have become essential in interpreting relational data across various domains, yet, they often struggle to generalize to unseen graph data that differs markedly from training instances. In this paper, we introduce a novel framework called General Retrieval-Augmented Graph Learning (RAGraph), which brings external graph data into the general graph foundation model to improve model generalization on unseen scenarios. On the top of our framework is a toy graph vector library that we established, which captures key attributes, such as features and task-specific label information. During inference, the RAGraph adeptly retrieves similar toy graphs based on key similarities in downstream tasks, integrating the retrieved data to enrich the learning context via the message-passing prompting mechanism. Our extensive experimental evaluations demonstrate that RAGraph significantly outperforms state-of-the-art graph learning methods in multiple tasks such as node classification, link prediction, and graph classification across both dynamic and static datasets. Furthermore, extensive testing confirms that RAGraph consistently maintains high performance without the need for task-specific fine-tuning, highlighting its adaptability, robustness, and broad applicability.

Overview of RAGraph: A General Retrieval-Augmented Graph Learning Framework

The paper introduces RAGraph, a novel graph learning framework that integrates Retrieval-Augmented Generation (RAG) techniques with Graph Neural Networks (GNNs). The RAGraph framework is designed to enhance the generalization capabilities of GNNs by leveraging external graph data, enabling improved performance on unseen graph data and diverse graph tasks such as node classification, link prediction, and graph classification. The core innovation involves the creation of a toy graph vector library, which captures key attributes like features and task-specific label information, and the adept retrieval of similar toy graphs during inference.

Main Contributions

1. Framework Design: RAGraph integrates retrieval mechanisms into GNNs, aiming to access and leverage external knowledge without task-specific fine-tuning. This plug-and-play module enables the GNNs to retrieve and integrate similar historical subgraphs, enhancing adaptability to new tasks and datasets.
2. Toy Graph Vector Library: The framework constructs a robust vector library of toy graphs. This library stores key information such as environmental, structural, and semantic details alongside node features and labels, used as a basis for retrieval.
3. Augmented Message-Passing: A significant aspect of RAGraph is the message-passing prompting mechanism, which integrates retrieved data into the graph learning process. This mechanism allows GNNs to enrich learning contexts by accessing relevant external knowledge dynamically.
4. Experimental Validation: The paper presents extensive evaluations demonstrating that RAGraph consistently outperforms existing state-of-the-art graph learning methods across multiple datasets and tasks without requiring extensive fine-tuning, showcasing its adaptability and robustness.

Key Insights and Results

• Performance Improvements: RAGraph outperformed several leading graph learning models, as evidenced by notable improvements in prediction tasks across multiple datasets, both dynamic and static. This highlights the framework's potential to enhance GNNs' generalization capabilities.
• Scalability and Efficiency: By employing an inverse importance sampling strategy and various data augmentation techniques, RAGraph effectively handles noise and variation in graph data, contributing to its scalability across datasets of different sizes and domains.
• Knowledge Integration: The ability of RAGraph to integrate both feature and label information into GNN models underscores its efficacy in enriching the knowledge space for graph-based learning tasks. This integration leads to better interpretability and accuracy in model predictions.
• Adaptability: The framework's tune-free nature and use of a parameter-free prompt mechanism make it particularly adaptable to unseen scenarios and diverse graph tasks, ensuring broad applicability across different domains.

Implications and Future Directions

The introduction of retrieval-augmented processes in graph learning opens new avenues for enhancing the interpretability and accuracy of GNNs, particularly in cases where external knowledge is beneficial. Practically, the framework could be applied to scenarios like network anomaly detection, rare disease diagnosis, and personalized recommendations, where data diversity and scarcity are significant challenges.

Theoretically, RAGraph offers a promising model for researching the interplay between external data retrieval and graph-based machine learning. Future work could explore more sophisticated retrieval metrics or extend retrieval to other structured data forms, such as hypergraphs or dynamic multigraphs.

In conclusion, RAGraph represents a significant stride in integrating retrieval mechanisms with graph learning models, offering a new perspective for improving graph learning tasks' efficacy and broadening the scope of GNNs in real-world applications.