Open Access

ARTICLE

Analyzing Dynamic Change in Social Network Based on Distribution-Free Multivariate Process Control Method

Yan Liu^1,*, Lian Liu¹, Yu Yan², Hao Feng¹, Shichang Ding³

1 State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, 450001, China.
2 The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
3 Goettingen University, Goettingen, 37077, Germany.

* Corresponding Author: Yan Liu. Email: .

Computers, Materials & Continua 2019, 60(3), 1123-1139. https://doi.org/10.32604/cmc.2019.05619

Abstract

Social organizations can be represented by social network because it can mathematically quantify and represent complex interrelated organizational behavior. Exploring the change in dynamic social network is essential for the situation awareness of the corresponding social organization. Social network usually evolves gradually and slightly, which is hard to be noticed. The statistical process control techniques in industry field have been used to distinguish the statistically significant change of social network. But the original method is narrowed due to some limitation on measures. This paper presents a generic framework to address the change detection problem in dynamic social network and introduces a distribution-free multivariate control charts to supervise the changing of social network. Three groups of network parameters are integrated together in order to achieve a comprehensive view of the dynamic tendency. The proposed approaches handle the non-Gaussian data based on categorizing and ranking. Experiments indicate that nonparametric multivariate procedure is promising to be applied to social network analysis.

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Citations