Betweenness to assess leaders in criminal networks: New evidence using the dual projection approach

Grassi, Rosanna; Calderoni, Francesco; Bianchi, Monica; Torriero, Anna

doi:10.1016/j.socnet.2018.08.001

Cited by 45 publications

(28 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To test Hypothesis 1, which examines if there is a significant amount of G in the network, we first converted the two-mode network data which link the two types of nodes collected in the survey (i.e., social network entities and goal node), into a one-mode, actor-by-actor matrix using the row projection technique (Everett & Borgatti, 2013; Grassi, Calderoni, Bianchi, & Torriero, 2019). The result of the data transformation is a goal striving adjacency matrix in which the ( i, j ) cell is the extent to which actors i and j share independent goal striving functions, and 0 if at least one actor i or j is not striving for the goal.…”

Section: Resultsmentioning

confidence: 99%

Network Goal Analysis of Social and Organizational Systems: Testing Dynamic Network Theory in Complex Social Networks

Westaby

Parr

2019

The Journal of Applied Behavioral Science

View full text Add to dashboard Cite

Grounded in dynamic network theory, this study examined network goal analysis (NGA) to understand complex systems. NGA provides new insights by inserting goal nodes into social networks. Goal nodes can also represent missions, objectives, or desires, thus having wide applicability. The theory ties social networks to goal nodes through a parsimonious set of social network role linkages, such as independent goal striving, system supporting, feedback, goal preventing, supportive resisting, and system negating (i.e., those who are upset with others in the pursuit). Moreover, we extend the theory’s system reactance role linkage to better account for constructive conflicts. Two complex systems were examined: a team’s mission and an individual’s work project. In support of dynamic network theory, using the Quadratic Assignment Procedure, results demonstrated significant shared goal striving, system supporting, and shared connections between goal striving and system supporting. These findings manifest what we coin as multipendence: Systems having some actions independently involved with goals, while others are dependently involved in the associated network. NGA also demonstrated that the goal nodes manifested strong betweenness centrality, indicating that goal striving and feedback links were connecting entities across the wider system. Strategies to plan network goal interventions are illustrated with implications for practice.

show abstract

Section: Resultsmentioning

confidence: 99%

Network Goal Analysis of Social and Organizational Systems: Testing Dynamic Network Theory in Complex Social Networks

Westaby

Parr

2019

The Journal of Applied Behavioral Science

View full text Add to dashboard Cite

show abstract

“…The dataset was extracted from this judicial act and is available as a 2-mode matrix on the UCINET [ 46 ] website (Link: https://sites.google.com/site/ucinetsoftware/datasets/covert-networks/ndranghetamafia2 ). The Infinito Operation dataset was investigated by Calderoni and his co-authors in several works [ 36 – 40 ]. From the original 2-mode matrix, we constructed the weighted and undirected graph Summits Network (SN) with 156 nodes and 1619 edges ( Table 2 ).…”

Section: Methodsmentioning

confidence: 99%

Criminal networks analysis in missing data scenarios through graph distances

Ficara

Cavallaro²,

Curreri³

et al. 2021

PLoS ONE

View full text Add to dashboard Cite

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.

show abstract

“…(2) to treat the network as if ties were possible within modes by adding empty rows for each node that was only represented in the columns of the original network and vice-versa (Bonacich 1991;Borgatti & Everett 1997), or to (3) dual project the social network by estimating ranks on each mode of the network separately before combining the network back into its original form (Everett 2016;Everett & Borgatti 2013;Grassi et al 2019). These conversion methods can interact with some centrality algorithms differently than others, but for PageRank and other variations of eigenvector centrality, each method returns nearly identical node ranks (Bonacich 1991).…”

Section: Pagerank Centrality On One Mode Network Projectionsmentioning

confidence: 99%

Comparing measures of centrality in bipartite patient-prescriber networks: A study of drug seeking for opioid analgesics

Aronson¹,

Yang²,

Odabaş³

et al. 2020

Preprint

View full text Add to dashboard Cite

Social network analysts typically study node prominence in bipartite social networks with methods that do not fully account the two-mode topology of the underlying social network. Recent studies have generated a number of algorithms that might estimate node prominence in bipartite graphs with greater accuracy; however, it is unclear how these algorithms perform on real world social networks. To provide a comparison of bipartite centrality algorithms and recommendations regarding their appropriate use cases, this article examines their performance in a real-world network of patient-provider ties. We build on a recent body of research showing that network centrality in patient-provider networks is a promising indicator of prescription drug-seeking. We measure centrality with recently-developed bipartite methods, and we find that BiRank and CoHITS provide significantly more robust measures of prescription drug-seeking and better predictors of subsequent opioid overdose than traditional centrality estimates, including PageRank in a one mode network projection. Our results suggest that these algorithms may be very useful for identifying centrality across network contexts.

show abstract

Betweenness to assess leaders in criminal networks: New evidence using the dual projection approach

Cited by 45 publications

References 36 publications

Network Goal Analysis of Social and Organizational Systems: Testing Dynamic Network Theory in Complex Social Networks

Network Goal Analysis of Social and Organizational Systems: Testing Dynamic Network Theory in Complex Social Networks

Criminal networks analysis in missing data scenarios through graph distances

Comparing measures of centrality in bipartite patient-prescriber networks: A study of drug seeking for opioid analgesics

Contact Info

Product

Resources

About