Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learning

Navigating Distribution Shifts in Graph Learning: A Comprehensive Survey

Introduction

Graph structural data, a significant aspect of numerous real-world domains such as biological networks, social networks, and recommendation systems, presents unique challenges due to its dynamic nature exemplified by evolving relationships and changing node attributes over time. This dynamic evolution often leads to distribution shifts in graph data, a phenomenon that significantly impairs the generalization and adaptation capabilities of graph learning models. Addressing distribution shifts in graph learning is pivotal for deploying effective models in real-world scenarios. This comprehensive survey explores the latest methodologies and strategies for graph learning under distribution shifts, primarily categorized into graph domain adaptation learning, graph out-of-distribution learning, and graph continual learning. Each category is dissected based on the observability of distributions in the inference stage and the availability of supervision information in the training stage, offering a systematic analysis of the existing solutions, and setting the stage for discussions on potential practical applications and future directions in this field.

Categorization of Graph Learning Methods Under Distribution Shifts

Graph Domain Adaptation Learning

Graph Domain Adaptation Learning focuses on adapting graph learning models from a source to a target domain where the graph data distributions differ. This adaptation ensures model proficiency in the target domain, which is crucial for applications across varying domains. The methods under this category are further classified into semi-supervised, unsupervised, and test-time graph transformation (adaptation) approaches, each tailored to specific scenarios of domain adaptability and knowledge transfer.

Graph Out-of-Distribution Learning

Graph Out-of-Distribution Learning addresses the challenge of learning from graphs with unseen distributions, aiming to generalize well to completely novel test graphs. This section introduces methods targeting Graph Out-of-Distribution Generalization, Detection, and Open-world Graph Learning. These approaches enable models to handle unseen classes and distributions effectively.

Graph Continual Learning

Graph Continual Learning deals with learning from a stream of graph data over time. This category emits strategies designed to incorporate new information from evolving graphs while retaining previously learned knowledge. Architectural, regularization, rehearsal, and hybrid approaches under this umbrella highlight mechanisms to counteract the forgetting of old tasks and ensure the seamless integration of new graph data into the learning process.

Methodological Insights and Frameworks

The survey presents a rich analysis of various graph learning methods under distribution shifts, discussing their theoretical foundations and practical implementations. Highlights include innovative frameworks like AdaGCN for graph domain adaptation, methodologies such as GraphDE for graph out-of-distribution detection, and novel concepts like ContinualGNN for graph continual learning. Each methodological insight contributes to understanding the complex nature of graph data and offers solutions to enhance model robustness and adaptability across different graph distribution shifts.

Applications and Future Directions

The implications of effectively addressing graph distribution shifts extend across multiple fields. Applications range from AI-aided drug discovery, where graph learning can accelerate the identification of new pharmaceutical compounds, to personalized recommendation systems which benefit from adapted and continually learning graph models. Furthermore, the integration of graph learning in intelligent transportation systems and open-world knowledge exploration exemplifies the broad utilitarian value of advancing this research area.

Looking forward, the survey identifies promising research directions including data-centric graph learning, cross-modality distribution shift exploration, and the formulation of comprehensive task-driven evaluation protocols. Additionally, fostering trustworthy graph learning under distribution shifts by enhancing aspects such as robustness, explainability, privacy, and fairness represents crucial future milestones.

Conclusion

This survey underscores the significance of tackling distribution shifts in graph learning to harness the full potential of graph structural data across a wide array of practical applications. By systematically categorizing existing methods, highlighting potential applications, and pointing towards uncharted research avenues, it lays a foundation for future investigations and developments in graph learning under distribution shifts. As the field progresses, addressing these challenges will undoubtedly lead to more robust, adaptive, and trustworthy graph learning models, propelling forward the capabilities of AI in handling complex real-world data dynamics.