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In this paper, we introduce CrimeGraphNet, a novel approach for linkprediction in criminal networks utilizingGraph Convolutional Networks (GCNs).Criminal networks are intricate and dynamic, with covert links that arechallenging to uncover. Accurate prediction of these links can aid in proactivecrime prevention and investigation. Existing methods often fail to capture thecomplex interconnections in such networks. They also struggle in scenarioswhere only limited labeled data is available for training. To address thesechallenges, we propose CrimeGraphNet, which leverages the power of GCNs forlink prediction in these networks. The GCNmodel effectively capturestopological features and node characteristics, making it well-suited for thistask. We evaluate CrimeGraphNet on several real-world criminal networkdatasets. Our results demonstrate that CrimeGraphNet outperforms existingmethods in terms of prediction accuracy, robustness, and computationalefAciency. Furthermore, our approach enables the extraction of meaningfulinsights from the predicted links, thereby contributing to a betterunderstanding of the underlying criminal activities. Overall, CrimeGraphNetrepresents a signiAcant step forward in the use of deep learning for criminalnetwork analysis.

CrimeGraphNet: Link Prediction in Criminal Networks with Graph Convolutional Networks