ASSIST:大型时间图中重要结构变化的自动总结

C. Chelmis, R. Dani
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引用次数: 2

摘要

在安全、金融、医疗保健和在线社交媒体等领域的众多应用中,检测数据中的异常值和异常情况至关重要。这种动态系统可以建模为随时间变化的图形。尽管在寻找网络与过去显著不同的时间点方面做了大量工作,但在描述或解释这些变化方面做的工作却很少。然而,在网络数据越来越大的大数据时代,能够总结这些变化是意义分析和根本原因分析的关键。为了解决这一差距,我们提出了一种新的方法来总结大型时间图中的重大结构变化。具体来说,我们提出了一种有效的方法,通过向用户展示对变化贡献最大的关键子图的摘要,帮助用户理解网络结构的急剧变化。对真实世界数据集的广泛评估显示了我们的方法在定量和定性上准确检测变化和发现“重要”结构以简洁地描述变化的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ASSIST: Automatic Summarization of Significant Structural Changes in Large Temporal Graphs
Detecting outliers and anomalies in data is vital in numerous applications in areas such as security, finance, health care, and online social media. Such dynamic systems can be modeled as graphs that change over time. Even though considerable work has been performed on finding points in time at which a network notably differs from its past, little work has been done on characterizing or explaining such changes. However, in the era of big data where networked data are getting bigger and bigger, being able to summarize such changes is key for sensemaking and root cause analysis. To address this gap, we present a novel approach to summarize significant structural changes in large temporal graphs. Specifically, we propose an efficient approach to help the user understand sharp changes in the structure of the network by presenting to her only a summary of key subgraphs that contribute most to the change. Extensive evaluation on real-world datasets with ground truth demonstrates both quantitatively and qualitatively the ability of our approach to accurately detect changes and discover "important" structures to succinctly describe the change.
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