  In this paper, we introduce CrimeGNN, a novel application of Graph Neural
Networks (GNNs) specifically designed to uncover hidden communities within
criminal networks. As criminal activities increasingly rely on complex network
structures, traditional methods of network analysis often fall short in
detecting the intricate and dynamic communities within these networks.
Leveraging the power of GNNs, CrimeGNN provides an advanced and specialized
solution to this problem. The model ingests a graph structure of a criminal
network, where vertices represent individuals and edges represent relationships
between them. CrimeGNN aims to identify a partition of the vertex set, such
that each subset represents a distinct community within the network, maximizing
the modularity function. Experimental results on several benchmark datasets
demonstrate the effectiveness of CrimeGNN, outperforming existing methods in
terms of both accuracy and computational efficiency. The proposed framework
offers significant potential for aiding law enforcement agencies in proactive
policing and crime prevention measures by providing a more in-depth
understanding of the structure and operation of criminal networks.
