基于自组织映射的图聚类与流图可视化

Prabin B. Lamichhane, W. Eberle
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引用次数: 0

摘要

许多现实世界的网络,如计算机网络、社交网络和物联网(loT),都可以用流(或动态)图来表示。这些流图的分析是分类、异常检测、社区检测、聚类和可视化任务的基础。本文使用一种无监督学习模型——自组织映射(SOM)对流图进行聚类和可视化。因此,SOM用于对高维图结构数据的异常检测技术进行可视化和解释。为此,本文将基于som的图聚类和可视化技术分为两个阶段。在第一阶段,我们使用各种现有的图形素描技术,如StreamS pot、SpotLight和SnapSketch,将流图嵌入到草图向量中。随后,在第二阶段,我们将草图向量输入传递到SOM中进行聚类,并将正常和异常图形流可视化,以解释异常检测技术。此外,基于som的可视化还有助于估计嵌入(或草图)技术的质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Self-Organizing Map-Based Graph Clustering and Visualization on Streaming Graphs
Many real-world networks, such as computer networks, social networks, and the Internet of Things (loT), can be represented by streaming (or dynamic) graphs. Analysis of these streaming graphs serves as the basis for classification, anomaly detection, community detection, clustering, and visual-ization tasks. This paper uses a Self-Organizing Map (SOM), an unsupervised learning model, to cluster and visualize streaming graphs. As a result, a SOM is used to visualize and interpret the anomaly detection technique on high-dimensional graph-structured data. For this, the SOM-based graph clustering and visualization technique is divided into two phases. In the first phase, we use various existing graph sketching techniques like StreamS pot, SpotLight, and SnapSketch to embed streaming graphs into sketched vectors. Later, in the second phase, we pass the sketched vector inputs into a SOM to cluster and visualize the normal and anomalous graph streams to interpret the anomaly detection technique. In addition, the SOM-based visualization also helps to estimate the quality of embedding (or sketching) techniques.
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