13,326 research outputs found
Disrupting resilient criminal networks through data analysis: The case of sicilian mafia
Compared to other types of social networks, criminal networks present particularly hard challenges, due to their strong resilience to disruption, which poses severe hurdles to LawEnforcement Agencies (LEAs). Herein, we borrow methods and tools from Social Network Analysis (SNA) to (i) unveil the structure and organization of Sicilian Mafia gangs, based on two real-world datasets, and (ii) gain insights as to how to efficiently reduce the Largest Connected Component (LCC) of two networks derived from them. Mafia networks have peculiar features in terms of the links distribution and strength, which makes them very different from other social networks, and extremely robust to exogenous perturbations. Analysts also face difficulties in collecting reliable datasets that accurately describe the gangs' internal structure and their relationships with the external world, which is why earlier studies are largely qualitative, elusive and incomplete. An added value of our work is the generation of two realworld datasets, based on raw data extracted from juridical acts, relating to a Mafia organization that operated in Sicily during the first decade of 2000s. We created two different networks, capturing phone calls and physical meetings, respectively. Our analysis simulated different intervention procedures: (i) arresting one criminal at a time (sequential node removal); and (ii) police raids (node block removal). In both the sequential, and the node block removal intervention procedures, the Betweenness centrality was the most effective strategy in prioritizing the nodes to be removed. For instance, when targeting the top 5% nodes with the largest Betweenness centrality, our simulations suggest a reduction of up to 70% in the size of the LCC. We also identified that, due the peculiar type of interactions in criminal networks (namely, the distribution of the interactions' frequency), no significant differences exist between weighted and unweighted network analysis. Our work has significant practical applications for perturbing the operations of criminal and terrorist networks
Human and social capital strategies for Mafia network disruption
Social Network Analysis (SNA) is an interdisciplinary science that focuses on
discovering the patterns of individuals interactions. In particular,
practitioners have used SNA to describe and analyze criminal networks to
highlight subgroups, key actors, strengths and weaknesses in order to generate
disruption interventions and crime prevention systems. In this paper, the
effectiveness of a total of seven disruption strategies for two real Mafia
networks is investigated adopting SNA tools. Three interventions targeting
actors with a high level of social capital and three interventions targeting
those with a high human capital are put to the test and compared between each
other and with random node removal. Human and social capital approaches were
also applied on the Barab\'asi-Albert models which are the one which better
represent criminal networks. Simulations showed that actor removal based on
social capital proved to be the most effective strategy, by leading to the
total disruption of the criminal network in the least number of steps. The
removal of a specific figure of a Mafia family such as the Caporegime seemed
also promising in the network disruption
Criminal networks analysis in missing data scenarios through graph distances
Data collected in criminal investigations may suffer from issues like: (i) incompleteness, due to the covert nature of criminal organizations; (ii) incorrectness, caused by either unintentional data collection errors or intentional deception by criminals; (iii) inconsistency, when the same information is collected into law enforcement databases multiple times, or in different formats. In this paper we analyze nine real criminal networks of different nature (i.e., Mafia networks, criminal street gangs and terrorist organizations) in order to quantify the impact of incomplete data, and to determine which network type is most affected by it. The networks are firstly pruned using two specific methods: (i) random edge removal, simulating the scenario in which the Law Enforcement Agencies fail to intercept some calls, or to spot sporadic meetings among suspects; (ii) node removal, modeling the situation in which some suspects cannot be intercepted or investigated. Finally we compute spectral distances (i.e., Adjacency, Laplacian and normalized Laplacian Spectral Distances) and matrix distances (i.e., Root Euclidean Distance) between the complete and pruned networks, which we compare using statistical analysis. Our investigation identifies two main features: first, the overall understanding of the criminal networks remains high even with incomplete data on criminal interactions (i.e., when 10% of edges are removed); second, removing even a small fraction of suspects not investigated (i.e., 2% of nodes are removed) may lead to significant misinterpretation of the overall network. Copyright
Disrupting resilient criminal networks through data analysis: The case of Sicilian Mafia
Compared to other types of social networks, criminal networks present particularly hard challenges, due to their strong resilience to disruption, which poses severe hurdles to Law-Enforcement Agencies (LEAs). Herein, we borrow methods and tools from Social Network Analysis (SNA) to (i) unveil the structure and organization of Sicilian Mafia gangs, based on two real-world datasets, and (ii) gain insights as to how to efficiently reduce the Largest Connected Component (LCC) of two networks derived from them. Mafia networks have peculiar features in terms of the links distribution and strength, which makes them very different from other social networks, and extremely robust to exogenous perturbations. Analysts also face difficulties in collecting reliable datasets that accurately describe the gangs’ internal structure and their relationships with the external world, which is why earlier studies are largely qualitative, elusive and incomplete. An added value of our work is the generation of two real-world datasets, based on raw data extracted from juridical acts, relating to a Mafia organization that operated in Sicily during the first decade of 2000s. We created two different networks, capturing phone calls and physical meetings, respectively. Our analysis simulated different intervention procedures: (i) arresting one criminal at a time (sequential node removal); and (ii) police raids (node block removal). In both the sequential, and the node block removal intervention procedures, the Betweenness centrality was the most effective strategy in prioritizing the nodes to be removed. For instance, when targeting the top 5% nodes with the largest Betweenness centrality, our simulations suggest a reduction of up to 70% in the size of the LCC. We also identified that, due the peculiar type of interactions in criminal networks (namely, the distribution of the interactions’ frequency), no significant differences exist between weighted and unweighted network analysis. Our work has significant practical applications for perturbing the operations of criminal and terrorist networks
The web of federal crimes in Brazil: topology, weaknesses, and control
Law enforcement and intelligence agencies worldwide struggle to find
effective ways to fight and control organized crime. However, illegal networks
operate outside the law and much of the data collected is classified.
Therefore, little is known about criminal networks structure, topological
weaknesses, and control. In this contribution we present a unique criminal
network of federal crimes in Brazil. We study its structure, its response to
different attack strategies, and its controllability. Surprisingly, the network
composed of multiple crimes of federal jurisdiction has a giant component,
enclosing more than a half of all its edges. This component shows some typical
social network characteristics, such as small-worldness and high clustering
coefficient, however it is much "darker" than common social networks, having
low levels of edge density and network efficiency. On the other side, it has a
very high modularity value, . Comparing multiple attack strategies, we
show that it is possible to disrupt the giant component of the network by
removing only of its edges or nodes, according to a module-based
prescription, precisely due to its high modularity. Finally, we show that the
component is controllable, in the sense of the exact network control theory, by
getting access to of the driver nodes.Comment: 9 pages, 5 figure
Growth and Containment of a Hierarchical Criminal Network
We model the hierarchical evolution of an organized criminal network via
antagonistic recruitment and pursuit processes. Within the recruitment phase, a
criminal kingpin enlists new members into the network, who in turn seek out
other affiliates. New recruits are linked to established criminals according to
a probability distribution that depends on the current network structure. At
the same time, law enforcement agents attempt to dismantle the growing
organization using pursuit strategies that initiate on the lower level nodes
and that unfold as self-avoiding random walks. The global details of the
organization are unknown to law enforcement, who must explore the hierarchy
node by node. We halt the pursuit when certain local criteria of the network
are uncovered, encoding if and when an arrest is made; the criminal network is
assumed to be eradicated if the kingpin is arrested. We first analyze
recruitment and study the large scale properties of the growing network; later
we add pursuit and use numerical simulations to study the eradication
probability in the case of three pursuit strategies, the time to first
eradication and related costs. Within the context of this model, we find that
eradication becomes increasingly costly as the network increases in size and
that the optimal way of arresting the kingpin is to intervene at the early
stages of network formation. We discuss our results in the context of dark
network disruption and their implications on possible law enforcement
strategies.Comment: 16 pages, 11 Figures; New title; Updated figures with color scheme
better suited for colorblind readers and for gray scale printin
Generalized Network Dismantling
Finding the set of nodes, which removed or (de)activated can stop the spread
of (dis)information, contain an epidemic or disrupt the functioning of a
corrupt/criminal organization is still one of the key challenges in network
science. In this paper, we introduce the generalized network dismantling
problem, which aims to find the set of nodes that, when removed from a network,
results in a network fragmentation into subcritical network components at
minimum cost. For unit costs, our formulation becomes equivalent to the
standard network dismantling problem. Our non-unit cost generalization allows
for the inclusion of topological cost functions related to node centrality and
non-topological features such as the price, protection level or even social
value of a node. In order to solve this optimization problem, we propose a
method, which is based on the spectral properties of a novel node-weighted
Laplacian operator. The proposed method is applicable to large-scale networks
with millions of nodes. It outperforms current state-of-the-art methods and
opens new directions in understanding the vulnerability and robustness of
complex systems.Comment: 6 pages, 5 figure
Undermining and Strengthening Social Networks through Network Modification
Social networks have well documented effects at the individual and aggregate
level. Consequently it is often useful to understand how an attempt to
influence a network will change its structure and consequently achieve other
goals. We develop a framework for network modification that allows for
arbitrary objective functions, types of modification (e.g. edge weight
addition, edge weight removal, node removal, and covariate value change), and
recovery mechanisms (i.e. how a network responds to interventions). The
framework outlined in this paper helps both to situate the existing work on
network interventions but also opens up many new possibilities for intervening
in networks. In particular use two case studies to highlight the potential
impact of empirically calibrating the objective function and network recovery
mechanisms as well as showing how interventions beyond node removal can be
optimised. First, we simulate an optimal removal of nodes from the Noordin
terrorist network in order to reduce the expected number of attacks (based on
empirically predicting the terrorist collaboration network from multiple types
of network ties). Second, we simulate optimally strengthening ties within
entrepreneurial ecosystems in six developing countries. In both cases we
estimate ERGM models to simulate how a network will endogenously evolve after
intervention
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