圖表摘要方法及其應用綜述（CS IR）
- 2020 年 4 月 6 日
- 筆記
雖然計算資源的進步使得處理大量數據成為可能，但是人類識別這些數據中的模式的能力並沒有相應地擴大。因此，用於壓縮和簡化數據的有效計算方法對於提取可操作的洞察力變得至關重要。 特別是，儘管數據摘要技術已經被廣泛研究，但是直到最近，總結互聯數據或圖形才變得流行起來 這個調查是一個結構化的，全面的概述國家的最先進的方法總結圖表數據。我們首先討論背後的動機，以及面臨的挑戰，圖表摘要。然後，我們根據作為輸入的圖的類型對摘要方法進行分類，並根據核心方法進一步組織每個類別。最後，我們討論了自動文摘在實際圖形領域中的應用，並通過描述該領域中一些尚未解決的問題得出結論。
原文題目：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
