Inductive and transductive link prediction for criminal network analysis

Ahmadi, Zahra; Nguyen, Hoang Huy; Zhang, Zijian; Bozhkov, Dmytro; Kudenko, Daniel; Jofre, Maria; Calderoni, Francesco; Cohen, Noa; Solewicz, Yosef A.

doi:10.1016/j.jocs.2023.102063

Cited by 2 publications

(1 citation statement)

References 35 publications

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“…Due to the lack of complete and accurate data [1,8,[17][18][19] some studies resort to machine learning and deep learning techniques to predict patterns and missing links that may unveil previously undisclosed relationships between members [20,21], potentially leading to more accurate network representations of the illicit organizations under investigation and consequently alleviating the development of misleading models [9].…”

Section: Introductionmentioning

confidence: 99%

Directed Criminal Networks: Temporal Analysis and Disruption

Anastasiadis,

Antoniou

2024

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We extend network analysis to directed criminal networks in the context of asymmetric links. We computed selected centralities, centralizations and the assortativity of a drug trafficking network with 110 nodes and 295 edges. We also monitored the centralizations of eleven temporal networks corresponding to successive stages of investigation during the period 1994–1996. All indices reach local extrema at the stage of highest activity, extending previous results to directed networks. The sharpest changes (90%) are observed for betweenness and in-degree centralization. A notable difference between entropies is observed: the in-degree entropy reaches a global minimum at month 12, while the out-degree entropy reaches a global maximum. This confirms that at the stage of highest activity, incoming instructions are precise and focused, while outgoing instructions are diversified. These findings are expected to be useful for alerting the authorities to increasing criminal activity. The disruption simulations on the time-averaged network extend previous results on undirected networks to directed networks.

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Section: Introductionmentioning

confidence: 99%

Directed Criminal Networks: Temporal Analysis and Disruption

Anastasiadis,

Antoniou

2024

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Criminal organizations exhibit hysteresis, resilience, and robustness by balancing security and efficiency

van Elteren,

Vasconcelos,

Lees

2024

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The interplay between (criminal) organizations and (law enforcement) disruption strategies is critical in criminology and social network analysis. Like legitimate businesses, criminal enterprises thrive by fulfilling specific demands and navigating their unique challenges, including balancing operational visibility and security. This study aims at comprehending criminal networks’ internal dynamics, resilience to law enforcement interventions, and robustness to changes in external conditions. Using a model based on evolutionary game theory, we analyze these networks as collaborative assemblies of roles, considering expected costs, potential benefits, and the certainty of expected outcomes. Here, we show that criminal organizations exhibit strong hysteresis effects, with increased resilience and robustness once established, challenging the effectiveness of traditional law enforcement strategies focused on deterrence through increased punishment. The hysteresis effect defines optimal thresholds for the formation or dissolution of criminal organisation. Our findings indicate that interventions of similar magnitude can lead to vastly different outcomes depending on the existing state of criminality. This result suggests that the relationship between stricter punishment and its deterrent effect on organized crime is complex and sometimes non-linear. Furthermore, we demonstrate that network structure, specifically interconnectedness (link density) and assortativity of specialized skills, significantly influences the formation and stability of criminal organizations, underscoring the importance of considering social connections and the accessibility of roles in combating organized crime. These insights contribute to a deeper understanding of the systemic nature of criminal behavior from an evolutionary perspective and highlight the need for adaptive, strategic approaches in policy-making and law enforcement to disrupt criminal networks effectively.

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Inductive and transductive link prediction for criminal network analysis

Cited by 2 publications

References 35 publications

Directed Criminal Networks: Temporal Analysis and Disruption

Directed Criminal Networks: Temporal Analysis and Disruption

Criminal organizations exhibit hysteresis, resilience, and robustness by balancing security and efficiency

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