图表摘要方法及其应用综述(CS IR)

虽然计算资源的进步使得处理大量数据成为可能,但是人类识别这些数据中的模式的能力并没有相应地扩大。因此,用于压缩和简化数据的有效计算方法对于提取可操作的洞察力变得至关重要。 特别是,尽管数据摘要技术已经被广泛研究,但是直到最近,总结互联数据或图形才变得流行起来 这个调查是一个结构化的,全面的概述国家的最先进的方法总结图表数据。我们首先讨论背后的动机,以及面临的挑战,图表摘要。然后,我们根据作为输入的图的类型对摘要方法进行分类,并根据核心方法进一步组织每个类别。最后,我们讨论了自动文摘在实际图形领域中的应用,并通过描述该领域中一些尚未解决的问题得出结论。

原文题目:Graph Summarization Methods and Applications: A Survey

原文: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.

原文作者:Tara Safavi

原文地址:https://arxiv.org/abs/1612.04883