The non-linear impact of data handling on network diffusion models

Nevin, James; Lees, Michael; Groth, Paul

doi:10.1016/j.patter.2021.100397

Cited by 3 publications

(1 citation statement)

References 38 publications

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“…The increasing emphasis on structural analysis based on criminal reports has led to the idea of finding a structural fingerprint to improve interventions by law enforcement [16,17]. Social networks of criminals, commonly constructed from biased data sources such as arrest records, telephone records, or informant data, illuminate divergent facets, engendering distinct network structures [18]. This inherent bias can distort evaluation of when a network is considered secure or efficient.…”

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confidence: 99%

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

Elteren,

Lees,

Vasconcelos

2024

Preprint

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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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mentioning

confidence: 99%

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

Elteren,

Lees,

Vasconcelos

2024

Preprint

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Data Integration Landscapes: The Case for Non-optimal Solutions in Network Diffusion Models

Nevin

Groth

Lees

2023

Lecture Notes in Computer Science

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An approach for analysing the impact of data integration on complex network diffusion models

Nevin

Groth

Lees

2023

Journal of Complex Networks

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Complex networks are a powerful way to reason about systems with non-trivial patterns of interaction. The increased attention in this research area is accelerated by the increasing availability of complex network data sets, with data often being reused as secondary data sources. Typically, multiple data sources are combined to create a larger, fuller picture of these complex networks and in doing so scientists have to make sometimes subjective decisions about how these sources should be integrated. These seemingly trivial decisions can sometimes have significant impact on both the resultant integrated networks and any downstream network models executed on them. We highlight the importance of this impact in online social networks and dark networks, two use-cases where data are regularly combined from multiple sources due to challenges in measurement or overlap of networks. We present a method for systematically testing how different, realistic data integration approaches can alter both the networks themselves and network models run on them, as well as an associated Python package (NIDMod) that implements this method. A number of experiments show the effectiveness of our method in identifying the impact of different data integration setups on network diffusion models.

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The non-linear impact of data handling on network diffusion models

Cited by 3 publications

References 38 publications

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

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

Data Integration Landscapes: The Case for Non-optimal Solutions in Network Diffusion Models

An approach for analysing the impact of data integration on complex network diffusion models

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