Robust link prediction in criminal networks: A case study of the Sicilian Mafia

Abstract Link prediction exercises may prove particularly challenging with noisy and incomplete networks, such as criminal networks. Also, the link prediction effectiveness may vary across different relations within a social group. We address these issues by assessing the performance of different link prediction algorithms on a mafia organization. The analysis relies on an original dataset manually extracted from the judicial documents of operation “Montagna”, conducted by the Italian law enforcement agencies against individuals affiliated with the Sicilian Mafia. To run our analysis, we extracted two networks: one including meetings and one recording telephone calls among suspects, respectively. We conducted two experiments on these networks. First, we applied several link prediction algorithms and observed that link prediction algorithms leveraging the full graph topology (such as the Katz score) provide very accurate results even on very sparse networks. Second, we carried out extensive simulations to investigate how the noisy and incomplete nature of criminal networks may affect the accuracy of link prediction algorithms. The experimental findings suggest the soundness of link predictions is relatively high provided that only a limited amount of knowledge about connections is hidden or missing, and the unobserved edges follow some kind of generative law. The different results on the meeting and telephone call networks indicate that the specific features of a network should be taken into careful consideration.