User:Morry24/draftarticle

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This is a draft article for my university class

Criminal Networks[edit]

A criminal network is comprised of a group of people who engage in criminal activities together, [1] law enforcement agencies can learn about these networks to target vulnerabilities and ideally dismantle the network using Social Network Analysis. [2] It is important to understand what a criminal network is and why they should be mapped in order to target these networks successfully. Ideally, once the members of a network have been mapped, the network can be crippled through the removal of key players. One program that can be used to map criminal networks, is called UCINet. This program has the capabilities to visually display how a network looks, which is then used to identify the key players within the network. [3] There are also a number of data analyses that can be done to better understand how a specific network functions and to find the best way to dismantle the network. [3]

What is a criminal network[edit]

Mapping and understanding how a criminal network functions is useful for intercepting organised criminal networks. Organised crime is important in the context of criminal networks because it specifically looks at how individuals connect to one another and the positions they hold within their network. In an Australian context, some of the most prominent organised crime groups are outlaw motorcycle gangs, mafia groups and drug cartels, with motorcycle gangs being of current concern in Australia.[4] Outlaw motorcycle gangs can be involved in illicit drugs and high violence crimes, which is why they can be so detrimental to society, targeting these networks can result in the individual motorcycle gang crumbling.[5] Other types of organised crime can include; money laundering, identity crimes, cyber crimes and corruption within governments and businesses. [5][6] Criminal networks can be used by law enforcement agencies to better understand how individuals within a group connect to one another and the different roles each individual plays.[7] Law enforcement agencies are able to gather information on a criminal network by using a variety of different methods, this can include; monitoring telephone calls, police surveillance, witness statements, trial records and police records.[8]

Importance of understanding criminal networks[edit]

The ability to understand the importance that individuals hold within a criminal network is essential in targeting a network. Understanding the roles that players within the network have, enables law enforcement agencies to cripple a network by only targeting key players that the network cannot or would struggle to function without.[7] The connections that are made between individuals can refer to different relationships, such as being connected through prison, the same gang, a neighbourhood, friends or family and can help law enforcement better understand different networks.[9] Additionally, understanding how criminal networks function and how they can be targeted is relevant to policy making decisions surrounding law enforcement and criminal policy making to better improve the criminal justice process.[10]

Social Network Analysis (SNA)[edit]

Social network analysis is able to identify the relationships between people within a network and is used within the criminology field. More information can be found here Social network analysis.

Mapping a criminal network using Social Network Analysis[edit]

Mapping a criminal network involves a variety of different components and uses Social Network Analysis in a criminal network context. By understanding how each of these components relates to one another, it is easier to understand a specific network as each network will function and look differently. Additionally, within the programs that are used to map networks, different types of analyses can be conducted to better understand the network and how it can be effectively dismantled.

Components in mapping a network[edit]

There are different components that make up a network, or that can be found in a network. This includes; nodes, ties, cliques and subgroups. A node represents an individual or actor within a network. [11] Ties are the direct links or relationships between nodes, a tie shows who is connected to who.[12] A clique requires at least three nodes within the network to be adjacent to each other node within that group, a clique will not always be present with a network. [13] Finally, different networks can include subgroups, a subgroup develops when a smaller number of nodes within a larger network are in close proximity with one another.[14]

Programs that map networks and criminal network analysis[edit]

One program that has the ability to map different criminal networks is called UCINet.[3] This program is able to run different types of analyses on a data set to determine scores associated with members of the network. The types of analyses that can be run, include; degree centrality scores, betweenness scores, closeness centrality, cohesiveness, density and geodesic distance.[14]

Degree centrality measures how many nodes an individual within the network is connected to [15] and shows how active and important a node is compared to others within the network.[7] Betweenness centrality is a measure of how often a node within the network falls on the shortest path between two other nodes and can show who holds a strategic position within the network. [15] Closeness centrality scores measure how close one node is to all other nodes within a network. [9] Closeness centrality can determine the most central node within a given network. [16] Cohesiveness is a measure of how connected a network is within itself, which can determine how easy it is for information to flow through the network. [17] Density refers to the total number of possible connections with a network, and like cohesiveness can provide information on how quickly information travels through the network and between nodes. [9][17] Geodesic distance is able to determine the shortest path between nodes, which is again similar to cohesiveness and can help in understanding how information flows through the network.[14]

  • Different types of centrality measures, A, B and D are relevant to this article. A) Betweenness Centrality B) Closeness Centrality and D) Degree Centrality.
    Different types of centrality measures, A, B and D are relevant to this article. A) Betweenness Centrality B) Closeness Centrality and D) Degree Centrality.

Potential positives and negatives of Criminal Network Analysis[edit]

Positives[edit]

One of the most beneficial aspects of criminal network analysis is the potential in disrupting or crippling the network.[18] Criminal network analysis can also provide key information that can be used to prosecute criminal offenders or assist in police investigations. [19] By understanding how different criminal groups function, their is the possibility to predict future crimes, which could prevent substantial harm to society and reduce criminal activity. [20]

Negatives[edit]

A key concern are the potential threats to the reliability and validity of the data that is collected for use within criminal network analysis, where the complexity of the data needs to be taken into account to prevent incorrect data analysis. [7] Another potential concern for criminal network analysis is the possibility to miss connections due to the availability of data, if there is a lack of information available then it can be difficult to connect the actors within a network. [11] There is also the possibility that key players may be missed, they can 'hide' within a network depending on how that specific network functions, if key players are missed, it can potentially threaten the overall success of crippling the network. [11] Lastly, actors within a network have the potential to learn from previous mistakes and re-organise and adapt how their network functions to change how it appears to outsiders, making it harder to identify actors and key players. [21]

See also[edit]

Centrality

Organised crime

Social network analysis

Social network analysis software

References[edit]

  1. ^ "Criminal Networks and Trust". www.organized-crime.de. Retrieved 2019-10-05.
  2. ^ Hashimi, S.; Bouchard, Martin (2017-11-15). "On to the next one? Using social network data to inform police target prioritization". Policing. 40: 768–782. doi:10.1108/PIJPSM-06-2016-0079.
  3. ^ a b c "Description". www.analytictech.com. Retrieved 2019-10-05.
  4. ^ Commonwealth Parliament, Canberra. "Chapter 2". www.aph.gov.au. Retrieved 2019-10-05.{{cite web}}: CS1 maint: url-status (link)
  5. ^ a b admin (2016-01-21). "Organised crime groups". Australian Criminal Intelligence Commission. Retrieved 2019-10-03.
  6. ^ "Organized crime". www.interpol.int. Retrieved 2019-10-06.
  7. ^ a b c d Rostami, Amir; Mondani, Hernan (2015-03-16). "The Complexity of Crime Network Data: A Case Study of Its Consequences for Crime Control and the Study of Networks". PLOS ONE. 10 (3): e0119309. doi:10.1371/journal.pone.0119309. ISSN 1932-6203. PMC 4361400. PMID 25775130.{{cite journal}}: CS1 maint: PMC format (link) CS1 maint: unflagged free DOI (link)
  8. ^ oxide (2017-11-03). "Crime and justice statistics". Australian Institute of Criminology. Retrieved 2019-10-05.
  9. ^ a b c Masys, Anthony J. (2014-02-10). Networks and Network Analysis for Defence and Security. Springer Science & Business Media. ISBN 9783319041476.
  10. ^ Viano, Emilio C. (2016-12-10). Cybercrime, Organized Crime, and Societal Responses: International Approaches. Springer. ISBN 9783319445014.
  11. ^ a b c Campana, Paolo (2016-04-05). "Explaining criminal networks: Strategies and potential pitfalls". Methodological Innovations. 9: 205979911562274. doi:10.1177/2059799115622748. ISSN 2059-7991.
  12. ^ Hawe, Penelope; Webster, Cynthia; Shiell, Alan (2004-12-01). "A glossary of terms for navigating the field of social network analysis". Journal of Epidemiology & Community Health. 58 (12): 971–975. doi:10.1136/jech.2003.014530. ISSN 0143-005X. PMID 15547054.
  13. ^ "4. Cliques, Clusters and Components - Social Network Analysis for Startups [Book]". www.oreilly.com. Retrieved 2019-10-06.
  14. ^ a b c Wasserman, Stanley; Faust, Katherine; Urbana-Champaign), Stanley (University of Illinois Wasserman (1994-11-25). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN 9780521387071.
  15. ^ a b "Social network analysis: Centrality measures". Cambridge Intelligence. 2014-12-03. Retrieved 2019-10-06.
  16. ^ "Closeness Centrality - an overview | ScienceDirect Topics". www.sciencedirect.com. Retrieved 2019-10-05.
  17. ^ a b "Introduction to Social Network Methods:  Chapter 7:  Basic Properties of Networks and Actors". faculty.ucr.edu. Retrieved 2019-10-05. {{cite web}}: no-break space character in |title= at position 40 (help)
  18. ^ Burcher, Morgan; Whelan, Chad (2017-05-01). "Social network analysis as a tool for criminal intelligence: Understanding its potential from the perspectives of intelligence analysts". Trends in Organized Crime. 21: 1–17. doi:10.1007/s12117-017-9313-8.
  19. ^ Berlusconi, Giulia; Calderoni, Francesco; Parolini, Nicola; Verani, Marco; Piccardi, Carlo (2016-04-22). "Link Prediction in Criminal Networks: A Tool for Criminal Intelligence Analysis". PLoS ONE. 11 (4). doi:10.1371/journal.pone.0154244. ISSN 1932-6203. PMC 4841537. PMID 27104948.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  20. ^ International Association of Crime Analysts. (2018). Social Network Analysis for  Law Enforcement. Overland Park, KS. https://crimegunintelcenters.org/wp-content/uploads/2018/07/iacawp_2018_02_social_network_analysis.pdf
  21. ^ Duijn, Paul A. C.; Kashirin, Victor; Sloot, Peter M. A. (2014-02-28). "The Relative Ineffectiveness of Criminal Network Disruption". Scientific Reports. 4: 4238. doi:10.1038/srep04238. ISSN 2045-2322.
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