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In this paper, we present CrimeGAT, a novel application of Graph AttentionNetworks (GATs) for predictive policing in criminal networks. Criminal networkspose unique challenges for predictive analytics due to their complex structure,multi-relational links, and dynamic behavior. Traditional methods often fail tocapture these complexities, leading to suboptimal predictions. To address thesechallenges, we propose the use of GATs, which can effectively leverage bothnode features and graph structure to make predictions. Our proposed CrimeGATmodel integrates attention mechanisms to weigh the importance of a node'sneighbors, thereby capturing the local and global structures of criminalnetworks. We formulate the problem as learning a function that maps nodefeatures and graph structure to a prediction of future criminal activity. Theexperimental results on real-world datasets demonstrate that CrimeGATout-performs conventional methods in predicting criminal activities, therebyproviding a powerful tool for law enforcement agencies to proactively deployresources. Furthermore, the interpretable nature of the attentionmechanisminGATs offers insights into the key players and relationships in criminalnetworks. This research opens new avenues for applying deep learning techniquesin the Aeld of predictive policing and criminal network analysis.

CrimeGAT: Leveraging Graph Attention Networks for Enhanced Predictive Policing in Criminal Networks