Learning to Reduce the Scale of Large Graphs: A Comprehensive Survey
Hongjia Xu, Liangliang Zhang, Yao Ma, Sheng Zhou, Zhuonan Zheng, Jiajun Bu · ACM Transactions on Knowledge Discovery from Data · 2025
Graph data, prevalent across domains like social networks, biological systems, and recommendation systems, presents significant challenges due to its large scale and complex structure. The advent of Graph Neural Networks (GNNs) has revolutionized graph data mining by effectively capturing node dependencies and neighborhood information. However, the computational complexity of processing large-scale graphs remains a major hurdle, as real-world graphs often consist of millions or even billions of nodes and edges. Efficient techniques like message passing and sampling have helped mitigate this issue, but memory and processing constraints persist. A promising approach to addressing these challenges is learning to reduce the size of large-scale graphs while retaining essential information, thus facilitating faster and more efficient graph data mining tasks, such as graph condensation, reduction, coarsening, summarization, and so on. Despite the differences in terminology, approaches under these topics share the same motivation: to generate smaller yet informative graphs that can replace the original large-scale datasets. In this article, we unify these approaches under the concept of Graph Scaling (GS), highlighting the shared motivation across multiple topics. Alongside this definition, to clarify the question of what principles should be followed when scaling a graph and how a scaled graph was formulated, we propose a taxonomy to methodically categorize and understand existing methods. Moreover, by organizing the dataset and evaluation metrics, we aim to provide a more comprehensive understanding of the GS methods from a practical perspective. Moving forward, we delve into the limitations and challenges of GS methods, identifying the shortcomings and potential in the literature. Finally, we conclude this article by outlining future directions and offering concise guidelines to inspire future research in this field. A full paper list and online resources about GS are available at https://github.com/Frostland12138/Awesome-Graph-Scaling .