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Social network analysis for crime prediction under social computing and deep learning technology.

Xinghua Cao1, Lin Zhang2

  • 1School of Humanities, Beijing University of Chinese Medicine, Beijing, 102488, China.

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Summary

A new Key Person Identification Model Based on Account Association (KPI-AA) effectively identifies key figures in criminal networks. This model uses graph neural networks (GNNs) to improve prediction of criminal behaviors and network vulnerabilities.

Keywords:
Crime predictionDeep learningKey person identificationSocial computingSocial network analysis

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Area of Science:

  • Social Computing
  • Network Science
  • Deep Learning

Background:

  • Criminal networks possess complex structures and high levels of concealment.
  • Identifying key figures and predicting behaviors within these networks is challenging.
  • Social computing and deep learning offer potential solutions for uncovering hidden network dynamics.

Purpose of the Study:

  • To propose a novel model for identifying key persons in criminal networks.
  • To enhance the prediction of criminal behaviors and network vulnerabilities.
  • To leverage graph neural networks for in-depth network analysis.

Main Methods:

  • Developed the Key Person Identification Model Based on Account Association (KPI-AA).
  • Combined local neighbor similarity and global edge betweenness metrics.
  • Utilized graph neural networks (GNNs) for node characterization and network analysis.

Main Results:

  • KPI-AA demonstrated superior propagation dynamics compared to baseline models in simulated networks.
  • The model showed resilience in network robustness tests, maintaining connectivity after significant node removal.
  • Achieved a Kendall's tau coefficient of 0.467 on the Twitter dataset, indicating strong ranking consistency.

Conclusions:

  • KPI-AA effectively identifies core members and predicts behaviors in criminal networks.
  • The model offers advantages in propagation speed, vulnerability identification, and ranking consistency.
  • Demonstrated scalability and feasibility for practical deployment in social security and criminal investigations.