Many real-world datasets can be naturally represented as graphs, spanning a wide range of domains. However, the increasing complexity and size of graph datasets present significant challenges for analysis and computation. In response, graph reduction techniques have gained prominence for simplifying large graphs while preserving essential properties. In this survey, we aim to provide a comprehensive understanding of graph reduction methods, including graph sparsification, graph coarsening, and graph condensation. Specifically, we establish a unified definition for these methods and introduce a hierarchical taxonomy to categorize the challenges they address. Our survey then systematically reviews the technical details of these methods and emphasizes their practical applications across diverse scenarios. Furthermore, we outline critical research directions to ensure the continued effectiveness of graph reduction techniques, as well as provide a comprehensive paper list at https://github.com/ChandlerBang/awesome-graph-reduction. We hope this survey will bridge literature gaps and propel the advancement of this promising field.

翻译：许多现实世界的数据集可以自然地表示为图，涵盖了广泛的应用领域。然而，图数据集的复杂性和规模日益增加，给分析和计算带来了显著挑战。为此，图缩减技术因能简化大规模图同时保留关键属性而受到重视。本综述旨在提供对图缩减方法（包括图稀疏化、图粗化与图凝聚）的全面理解。具体而言，我们为这些方法建立了统一定义，并引入层次化分类体系对它们所解决的挑战进行归类。综述随后系统梳理了这些方法的技术细节，并强调其在不同场景下的实际应用。此外，我们概述了关键研究方向以确保图缩减技术的持续有效性，并在https://github.com/ChandlerBang/awesome-graph-reduction提供了全面的论文列表。希望本综述能填补文献空白，推动这一前景广阔的领域发展。