Abstract

Criminal network activities, which are usually secret and stealthy, present certain difficulties in conducting criminal network analysis (CNA) because of the lack of complete datasets. The collection of criminal activities data in these networks tends to be incomplete and inconsistent, which is reflected structurally in the criminal network in the form of missing nodes (actors) and links (relationships). Criminal networks are commonly analyzed using social network analysis (SNA) models. Most machine learning techniques that rely on the metrics of SNA models in the development of hidden or missing link prediction models utilize supervised learning. However, supervised learning usually requires the availability of a large dataset to train the link prediction model in order to achieve an optimum performance level. Therefore, this research is conducted to explore the application of deep reinforcement learning (DRL) in developing a criminal network hidden links prediction model from the reconstruction of a corrupted criminal network dataset. The experiment conducted on the model indicates that the dataset generated by the DRL model through self-play or self-simulation can be used to train the link prediction model. The DRL link prediction model exhibits a better performance than a conventional supervised machine learning technique, such as the gradient boosting machine (GBM) trained with a relatively smaller domain dataset.

Highlights

  • Research related to graph theory and network analysis in the field of social science [1] pioneered the development of social network analysis (SNA) methodologies

  • An experiment was conducted to compare the performance of the link prediction deep reinforcement learning (DRL) model (Figure 1) against the gradient boosting machine (GBM) model (Figure 2), both of which are based on a binary classification task

  • The GBM model was used as the baseline model in comparison with the DRL model for link prediction

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Summary

Introduction

Research related to graph theory and network analysis in the field of social science [1] pioneered the development of social network analysis (SNA) methodologies. The SNA methodologies combine graph theory with the application of analytical techniques and visualization tools developed for the analysis of social networks and other networks [2]. In particular, exhibit a relatively high propensity to have hidden or missing links because of the covert and stealthy nature of criminal activities [3]. This characteristic of criminal networks is often due to incomplete, incorrect, and inconsistent captured data, either caused by deliberate deception on the part of criminals or unintentional human error by law enforcement personnel. The common practise and techniques of predicting potential links between nodes

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