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A likelihood-based monitoring method for social networks with auto-correlated labeled Poisson model

Esmaeeli, H ; Sharif University of Technology | 2024

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  1. Type of Document: Article
  2. DOI: 10.1080/03610926.2024.2408560
  3. Publisher: 2024
  4. Abstract:
  5. Social network monitoring aims to detect any significant change in the structure of communications from the normal condition. The normal condition can be a set of networks considered as a reference set. This set is represented in dynamic cases by a probability model. Statistical process control methods can be applied to signal changes in the reference set. These signals can be useful for decision-makers in various applications, from marketing analysis to criminal investigations. In most of the developed models, the networks are assumed independent over time. However, this assumption may be questioned in real-world applications. Accordingly, this article formulates the social network as an auto-correlated labeled Poisson model and monitors it using a control method based on likelihood statistics. A simulation study is performed to evaluate the performance of the proposed methods in terms of the average run length criterion. The obtained results revealed that the proposed methods can reliably detect significant changes. © 2024 Taylor & Francis Group, LLC
  6. Keywords:
  7. Social network ; Auto correlation ; Control methods ; Labeled network ; Likelihood statistic ; Monitoring methods ; Network Monitoring ; Normal condition ; Poisson model ; Reference set
  8. Source: Communications in Statistics - Theory and Methods ; 2024 ; 03610926 (ISSN)
  9. URL: https://www.tandfonline.com/doi/abs/10.1080/03610926.2024.2408560