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