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Graph Summarization Methods and Applications: A Survey
arXiv - CS - Databases Pub Date : 2016-12-14 , DOI: arxiv-1612.04883
Yike Liu, Tara Safavi, Abhilash Dighe, Danai Koutra

While advances in computing resources have made processing enormous amounts of data possible, human ability to identify patterns in such data has not scaled accordingly. Efficient computational methods for condensing and simplifying data are thus becoming vital for extracting actionable insights. In particular, while data summarization techniques have been studied extensively, only recently has summarizing interconnected data, or graphs, become popular. This survey is a structured, comprehensive overview of the state-of-the-art methods for summarizing graph data. We first broach the motivation behind, and the challenges of, graph summarization. We then categorize summarization approaches by the type of graphs taken as input and further organize each category by core methodology. Finally, we discuss applications of summarization on real-world graphs and conclude by describing some open problems in the field.

中文翻译:

图汇总方法和应用:调查

虽然计算资源的进步使得处理大量数据成为可能,但人类识别此类数据中模式的能力并没有相应地扩展。因此,用于压缩和简化数据的高效计算方法对于提取可操作的见解变得至关重要。特别是,虽然数据汇总技术已被广泛研究,但直到最近,汇总互连数据或图形才变得流行。本调查是对最先进的图形数据汇总方法的结构化、全面概述。我们首先讨论图摘要背后的动机和挑战。然后,我们根据作为输入的图形类型对汇总方法进行分类,并通过核心方法进一步组织每个类别。最后,
更新日期:2020-04-03
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