图约简综合综述：稀疏化、粗化与凝聚
A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensation
AI总结：
本文系统综述了图稀疏化、图粗化和图凝聚三类图约简方法，提出统一定义与分层分类法，梳理技术细节、应用场景和未来研究方向，并附有完整论文列表。
摘要：
许多现实世界的数据集可以自然地表示为图，涵盖广泛的领域。然而，图数据集日益增长的复杂性和规模给分析和计算带来了重大挑战。为此，图约简（或称图摘要）因能够在保留基本属性的同时简化大型图而受到越来越多的关注。在本综述中，我们旨在提供对图约简方法的全面理解，包括图稀疏化、图粗化和图凝聚。具体而言，我们为这些方法建立了统一的定义，并引入分层分类法来归类它们所解决的挑战。随后，本综述系统地回顾了这些方法的技术细节，并强调了它们在多种场景中的实际应用。此外，我们概述了确保图约简技术持续有效性的关键研究方向，并在 \url{https://github.com/Emory-Melody/awesome-graph-reduction} 提供了完整的论文列表。我们希望本综述能够弥合文献空白，推动这一有前景领域的发展。
英文摘要：
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, or graph summarization, has 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 \url{https://github.com/Emory-Melody/awesome-graph-reduction}. We hope this survey will bridge literature gaps and propel the advancement of this promising field.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
