  In this paper, we introduce CrimeGraphNet, a novel approach for link
prediction in criminal networks utilizingGraph Convolutional Networks (GCNs).
Criminal networks are intricate and dynamic, with covert links that are
challenging to uncover. Accurate prediction of these links can aid in proactive
crime prevention and investigation. Existing methods often fail to capture the
complex interconnections in such networks. They also struggle in scenarios
where only limited labeled data is available for training. To address these
challenges, we propose CrimeGraphNet, which leverages the power of GCNs for
link prediction in these networks. The GCNmodel effectively captures
topological features and node characteristics, making it well-suited for this
task. We evaluate CrimeGraphNet on several real-world criminal network
datasets. Our results demonstrate that CrimeGraphNet outperforms existing
methods in terms of prediction accuracy, robustness, and computational
efAciency. Furthermore, our approach enables the extraction of meaningful
insights from the predicted links, thereby contributing to a better
understanding of the underlying criminal activities. Overall, CrimeGraphNet
represents a signiAcant step forward in the use of deep learning for criminal
network analysis.
