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A novel approach based on multiple correspondence analysis for monitoring social networks with categorical attributed data

机译:一种基于多对应分析的新方法,用于监控与分类归属数据的社交网络

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摘要

In in most cases, the distribution of communications is unknown and one may summarize social network communications with categorical attributes in a contingency table. Due to the categorical nature of the data and a large number of features, there are many parameters to be considered and estimated in the model. Hence, the accuracy of estimators decreases. To overcome the problem of high dimensionality and unknown communications distribution, multiple correspondence analysis is used to reduce the number of parameters. Then the rescaled data are studied in a Dirichlet model in which the parameters should be estimated. Moreover, two control charts, Hotelling's T-2 and multivariate exponentially weighted moving average (MEWMA), are developed to monitor the parameters of the Dirichlet distribution. The performance of the proposed method is evaluated through simulation studies in terms of average run length criterion. Finally, the proposed method is applied to a real case.
机译:在大多数情况下,通信的分发是未知的,并且可以将与差不呈表中的分类属性总结社交网络通信。由于数据的分类性和大量功能,在模型中有许多参数估计。因此,估算器的准确性降低。为了克服高维度和未知通信分布的问题,使用多个对应分析来减少参数的数量。然后在估计参数的Dirichlet模型中研究重新定义的数据。此外,开发了两个控制图,Hotelling的T-2和多变量指数加权移动平均(MEWMA)以监测Dirichlet分布的参数。通过在平均运行长度标准方面通过仿真研究评估所提出的方法的性能。最后,所提出的方法应用于实际情况。

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