论文标题
图摘要
Graph Summarization
论文作者
论文摘要
高度相互连接的数据集的持续和快速增长既是庞大又复杂的，呼吁开发适当的处理和分析技术。凝结和简化此类数据集的一种方法是图摘要。它表示一系列特定于应用程序的算法，旨在将图形转换为更紧凑的表示形式，同时保留结构模式，查询答案或特定的属性分布。由于此问题对于研究图形拓扑的几个领域很常见，因此已经提出了不同的方法，例如聚类，压缩，采样或影响检测，主要是基于统计和优化方法。本章的重点是查明主要的图形摘要方法，但尤其是专注于最新的方法和有关此主题的新研究趋势，但尚未涉及以前的调查。
The continuous and rapid growth of highly interconnected datasets, which are both voluminous and complex, calls for the development of adequate processing and analytical techniques. One method for condensing and simplifying such datasets is graph summarization. It denotes a series of application-specific algorithms designed to transform graphs into more compact representations while preserving structural patterns, query answers, or specific property distributions. As this problem is common to several areas studying graph topologies, different approaches, such as clustering, compression, sampling, or influence detection, have been proposed, primarily based on statistical and optimization methods. The focus of our chapter is to pinpoint the main graph summarization methods, but especially to focus on the most recent approaches and novel research trends on this topic, not yet covered by previous surveys.
