Exploring Influence Maximization in Criminal Social Networks to Identify Key Individuals and Patterns

Criminal networks are the social networks that show the individual and their links involved in illegal activities. The influence maximization (IM) problem aims to find the top influential nodes in social networks. Existing studies of criminal networks do not utilize the IM problem to understand its dynamics. The proposed work introduces IM to investigate its role in criminal social networks and proposes an algorithm IM-C that finds the key influential nodes in criminal social networks. This work examines key influencers and their influence on criminal networks. Further, by introducing IM, this study inspects the role of IM in geographic impact, resilience and vulnerability in criminal networks and the influence of centralities-based key users. The experiments are conducted on six datasets, and the experimental results show that the proposed algorithm outperformed the benchmark algorithms. The findings of key users, their influence, geographic impact, resilience and vulnerability provide insights into disrupting criminal networks and designing targeted intervention strategies for crime reduction. Thus, through experiment results, this work demonstrates that IM in criminal networks provides an impactful novel approach to uncover insights such as identifying key influencers, understanding their impact, and assessing network resilience and vulnerability.