Last year global deaths from terrorism increased by over 20% (GTI 2024), amplifying the need for careful scientific studies of this pernicious problem. Traditional scholarly approaches to terrorism frame violence as instrumental – the result of an individual-level, rational choice (Arendt 1969). We apply a social network framework to better understand the phenomenon of radical violence, which is helpful at identifying flows in relationships between groups, organizations, and nations (Chua 2024). Terrorism research, however, has been comparatively slow to adopt social network analysis, because relationships between groups are hard to observe (McMillan, Felmlee, & Braines 2020). Bipartite terrorist studies apply social network analysis to actors connected indirectly by the attributes of their actions, such as their 1) attack locations, 2) attack target, and 3) methods of violence. Several advantages include: 1) the ease of visualizing terrorist group two-mode networks, 2) identifying central actors, locations, methods, targets, and 3) detecting terrorist network subgroups. Bipartite networks also can capture largely invisible relationships between terrorist groups to understand better which groups are allies, splinters, and adversaries (Yarlagadda, Felmlee, Verma, & Gartner 2018). Such an approach uncovers the critical underlying, structural dynamics that drive effective counter terrorism policy (Corradi, Felmlee, & Gartner 2024; Verma et al. 2019). We discuss how to apply this approach to address critical terrorism study questions. We provide illustrations using data from multiple countries, demonstrating the insights gained from using bipartite analyses, and showing how this approach complements traditional terrorism studies, rational choice narratives, as well as newer artificial intelligence research.

In its report dated March 27, 2017, Transnational Crime and the Developing World, Global Financial Integrity estimated the revenues of the illicit drug market to range between $426 billion and $652 billion, making it the second most lucrative illicit market in the world. This figure, largely driven by traditional drug trafficking, is also influenced by virtual drug markets on the darknet, which have been in existence for over a decade. Yet, this area remains relatively underexplored in humanities research.

Started in 2023, my investigation relies on more than forty semi-structured interviews conducted with darknet users via specialized forums and cryptomarkets—an empirical dataset that is nearly unparalleled in social sciences research on the darknet at an international level (as confirmed by scholars such as James Martin, Rasmus Munksgaard, and Kyung-Shick Choi). By adopting an ethnographic approach, I contextualized these interviews with in situ observations of platform dynamics and user interactions.

The existence of such networks, which manage to endure over time, raises questions about how relationships between their various actors are formed and maintained. It also implies the establishment of a stable digital infrastructure that is not only technical but also social, and even ideological.

My contribution will illustrate how these criminals succeed in forging lasting connections that go beyond mere illegal transactions for financial gain. It will then demonstrate how they contribute to disrupting the conventional understanding of drug trafficking.

In the outlaw biker world, established members act as mentors to novice affiliates. Mentorship may be limited to subcultural mores, but may also include criminal tutelage. This study applies a new measure of criminal mentorship based on observed patterns of co-offending to examine the prevalence of criminal mentorship in the OMCG member population, and the extent to which that mentorship aligns with the club’s formal organization. To ascertain the importance of formal hierarchy and informal social status we estimate a series of logistic regression quadratic assignment procedure (LR-QAP) models including measures of dyadic similarity in formal rank and degree centrality (Krajewski, Dellaposta and Felmlee, 2022). We next describe the distribution, stability and diversity of OMCG members’ co-offender choice, using the co-offending stability measure (CSM) (McGloin et al, 2008) and weighted co-offending ego network diversity (Adamse, Blokland and Eichelsheim, 2024). Finally, we apply the TF*IDF dyadic difference (TIDD) (Adamse, Blokland and Eichelsheim, 2024) to assess the relative importance of each member of a co-offending dyad in the others’ co-offending ego network. Preliminary findings show ample co-offending among outlaw bikers, yet little overall co-offender stability. Co-offender stability however, shows substantial individual variation, and TIDD findings suggest criminal mentorship among OMCGs.

Human trafficking is considered a significant global crisis, with the 2024 UNODC Global Report on Trafficking in Persons estimating that 27 million individuals are exploited for labour, services, and commercial sex. Due to the hidden and decentralized nature of trafficking operations, the available data on these networks is highly limited, sparse, and fragmented. This lack of comprehensive data complicates efforts to understand trafficking operations and analyze their dynamics. To mitigate these challenges, we utilize a key tool in Social Network Analysis—link prediction methods—to infer missing or future links within a trafficking network with the aim of providing a more complete network structure for further analysis. Our approach introduces a novel learning-based link prediction algorithm that applies a graph-based deep learning model to uncover hidden connections in a real-world human trafficking dataset. We compare the model’s performance against traditional link prediction methods, such as similarity-based measures and probabilistic methods. By identifying missing connections, our approach reconstructs a more complete network representation. This enriched network model would enable law enforcement agencies to prioritize high-risk connections, track evolving trafficking patterns, and disrupt the network more effectively. Additionally, policymakers could leverage these insights to implement more targeted and data-driven intervention strategies. Our findings highlight the advantage of learning-based link prediction approaches over traditional methods in analyzing trafficking networks, providing a powerful support tool for strengthening anti-trafficking efforts.

Criminal desistance—the process by which individuals cease engaging in criminal behavior—remains a critical challenge in criminology and public policy. This study applies the Ising model, network comparison tests, and econometric regressions to analyze different pathways of recidivism and desistance using a dataset of more than 17,000 incarcerated individuals assessed through a validated risk factor instrument. The dataset includes 43 binary risk indicators covering criminal history, personality traits, education, substance abuse, social background, among other dimensions.

By modeling the statistical dependencies between risk factors using the Ising model, we identify key activation patterns that distinguish risk profiles. We further apply network comparison techniques to examine whether different subgroups exhibit structurally distinct risk networks. Finally, we incorporate logistic regression models to assess how these network structures predict recidivism outcomes. This multi-method approach allows us to move beyond traditional summative risk assessments by revealing interaction effects and latent structures within risk factors.

Preliminary findings suggest that certain risk factors act as structural hubs maintaining criminal behavior, while others serve as critical tipping points for desistance. This integrated network-based and econometric approach provides a novel framework for understanding the mechanisms underlying desistance and has direct implications for tailoring rehabilitation policies to different risk profiles.

By leveraging advanced statistical and network models, this study contributes to criminology and public policy by offering a data-driven perspective on recidivism prevention, highlighting the complex interdependencies shaping criminal trajectories.

Deals among organized crime groups (OCGs) can turn sour and may lead to conflict – sometimes violent. Yet, conflicts can be set aside when a new, maybe lucrative, joint opportunity arises. Building on previous works on co-offending and network violence among OCGs, we bring together cooperation and conflict and study their co-evolution over time. For this, we leverage police record data (2004-2015) provided by Thames Valley Police – the largest non-metropolitan police force in England and Wales. The data consist of 25,977 organized crime-related events in which at least one OCG member was involved. In the study, we focus on 147 OCGs, who were involved in 79 cooperation and 141 conflict events.

We study these events using relational event analysis through dynamics network actor models (DyNAMs). These models were proposed for relational event analysis from an actor-oriented perspective. To be able to study the rate of cooperation in this modeling framework, we propose new methodology to estimate rate models for undirected relational events using ideas from multiple imputation. Having estimated both rate models and partner choice models for cooperation and conflict, we find a strong interdependence between the two criminal networks among the OCGs. Yet, the event rate and partner choice of the groups are differentially affected by their network position (e.g., degree, embeddedness) and partner characteristics (e.g., primary criminal activity) for the two types of relational events. As the co-evolution of cooperation and conflict in the criminal context so far remained largely unexplored, this study fills a gap in our understanding of the dynamics underpinning organized crime operations.

Aims

This study explores the duality between criminal actors and relational hyperevents in an Italian mafia network, focusing on co-offending and communication. Relational hyperevents, defined as time-stamped interactions between two or more actors, provide a granular view of network evolution.

We distinguish two ontologically different event types: communication (sharing information/resources to commit a crime) and co-offending (the crime’s realization). We argue that these event types are mutually constitutive—communication precedes co-offending in organizing criminal activity. This reasoning extends beyond criminal networks to scientific, political, and artistic networks, where collaborative interactions (e.g., communication) precede outcomes (e.g., publications, legislation, or artistic productions).

To test this, we analyze three multiplex relational mechanisms—triadic closure, interaction repetition, and interaction accumulation—alongside their uniplex counterparts and exogenous factors (gender, age, leadership, locale affiliation, and kinship). Gender, age, and leadership are examined through selection/activity and homophily, while kinship and locale affiliation are assessed in relation to communication and co-offending.

Data & Methods

We use court records from Italian mafia investigation Infinito. Infinito documents the 'Ndrangheta’s drug-trafficking and territorial expansion in Lombardy, comprising 353 actors and several hundred events.

We apply Relational Hyper-Event Models (RHEM), which conceptualize polyadic interactions as hyperedges in a hypergraph. RHEM, combined with high-temporal-resolution data, allows us to model the interplay between co-offending and communication. Using the eventnet software package, we operationalize relational mechanisms and generate alternative (unobserved) non-events. We then estimate effects via a Cox proportional hazards model, identifying what characteristics make future events more or less likely.

Results

Preliminary results indicate that the network is held together primarily by communication ties, while co-offending is fragmented, sometimes occurring as single-actor crimes. Ignoring the relational hyperevent nature of the data could lead to significant information loss. Communication events (meetings and phone calls) are moderately correlated, and co-offending lags behind communication events.

Our RHEM analysis supports our hypothesis: communication precedes co-offending, especially in interaction repetition (within actor subsets). Notably, we find no evidence of triadic closure, contradicting existing social and criminal network research. Instead, interaction repetition explains observed closure effects. Additionally, uniplex interaction repetition and age- and leadership-based activity and homophily are significant for both event types.

Conclusions

We demonstrate RHEM’s utility in criminal network research and propose that traditional triadic closure effects may be artifacts of data aggregation rather than genuine actor tendencies. This has implications for criminal network studies using archival data.

Our findings bridge the gap between literature on the social organization of crime and co-offending networks, with potential applications in law enforcement, particularly in designing time-sensitive intervention strategies. Lastly, we suggest that distinguishing between process-oriented and outcome-oriented events can enhance network research in various domains, such as scientific collaboration and co-authorship analysis.

Urbanization has profoundly reshaped population distribution, with over half of the global population now living in cities, and has created new challenges in understanding urban issues such as crime. Despite this urban shift, the interplay between population dynamics, mobility, and crime remains poorly understood. In this work, we investigate how commuter flows, as a proxy for human mobility, can quantify the attractiveness of cities and shed light on differences in crime rates across urban regions in Great Britain.

We use commuting pattern data to explore criminological theories connecting population dynamics with crime [1]. While cities attract more people, they also experience higher levels of social anonymity, reduced community cohesion, and increased opportunities for crime [2, 3]. We employ journey-to-work census data for over 8 million workers in the UK to construct a directed network where nodes represent cities and edges capture commuter flows. We hypothesize that city attractiveness, as measured through the network, explains the non-linearity in how crime scales with population. We perform probabilistic scaling analysis [4] to assess the non-linearity in the relationships between attractiveness, commuter numbers, and crime types.

Our results reveal a positive correlation between commuter attractiveness and crime rates, with significant variations across crime types. Hub cities with high commuter inflows exhibit higher crime rates, supporting theories that transient populations weaken social control. Interestingly, certain crimes, such as theft, display superlinear scaling with attractiveness, while others, like burglary, align more closely with static population metrics.

This work advances the literature on urban crime by combining mobility network data and scaling analysis with criminological theories. By quantifying city attractiveness and examining its influence on crime, we present a novel framework for exploring the mechanisms underlying urbanization.

[1] Marcos Oliveira. “More crime in cities? On the scaling laws of crime and the inadequacy of per capita rankings—a cross-country study”. In: Crime Science 10.1 (2021), pp. 1–13.

[2] Claude S Fischer. “Toward a subcultural theory of urbanism”. In: American journal of Sociology 80.6 (1975), pp. 1319–1341. [3] Louis Wirth. “Urbanism as a Way of Life”. In: American journal of sociology 44.1 (1938), pp. 1–24.

[4] Jorge Leitao et al. “Is this scaling nonlinear?” In: Royal Society open science 3.7 (2016), p. 150649.

Crime is a complex phenomenon influenced by social, spatial, and contextual factors. Traditional statistical methods have long been used to analyse crime data, yet network-based approaches remain underexplored despite the inherently relational nature of criminal activity. Representing crime data as a network enables a more comprehensive understanding of its structure, capturing interactions between crimes, locations, and offenders that traditional methods often overlook.

This study examines a mid-size British police force dataset containing offender-victim relationships from crimes committed over a 46-months period. Given the dataset's richness, two network representations are constructed: a co-offending network, where a link between two suspects is established if they participated in the same crime event, and a directed network that links offenders to victims. To uncover latent structures within the data, we apply latent variable models that help provide deeper insights into broader crime patterns.

Specifically, we investigate how exposure to violence influences the behaviour of non‐violent offenders by assuming that an individual’s propensity for violent acts is influenced by the violent behaviours of connected actors and their position within a latent social space. Moreover, recognising that offender connections often arise from shared social, spatial, or contextual factors, we also cluster network edges to reveal underlying structures. This approach would allow to better understand the latent environments in which criminal associations form and explains why offenders participate in the same events. Ultimately, these methods offer valuable insights into the mechanisms driving criminal behaviours, providing a clearer, more nuanced perspective on UK crime patterns.

Purpose: In 2021, the US Marshals Service (USMS) partnered with law enforcement agencies across Galveston County, TX to conduct Operation Washout (OW), a 10-day operation intended to reduce violent crime through the execution of arrest warrants targeting gang members, firearm, and drug law violators. This study examines the impact of OW on the co-offending relationships of OW arrestees.

Methods: Arrest data for all of Galveston County are scraped from Police2Citizen Daily Bulletins and used to construct pre- and post-OW co-offending networks. These networks consist of individuals arrested during the intervention period, as well as all co-offenders within the first two neighborhoods. Stochastic actor-oriented models are estimated to model the rate that new co-offending relationships form following OW.

Results: Fugitives arrested as part of OW occupied brokerage positions in the network. Arrest during the intervention precedes a reduction in the rate that new co-offending relationships are formed. However, the rate that fugitives established new co-offending relationships was largely resistant to arrest.

Conclusions: Consistent with research on Operation Triple Beam, USMS-led fugitive operations appear to resemble broker targeting strategies proposed by network criminologists. Despite the selection of appropriate operational targets, fugitives’ ability to forge new co-offending relationships remains unimpeded by OW arrest.