Node Removal in Criminal Networks

Petersen, Rasmus Rosenqvist; Rhodes, Christopher J.; Wiil, Uffe Kock

doi:10.1109/eisic.2011.57

Cited by 20 publications

(12 citation statements)

References 7 publications

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“…Callahan et al [11] reshape networks by identifying a set of nodes to remove that jointly maximize the network wide degree centrality which extends degree centrality of nodes to a network as a whole. Petersen et al [12] develop methods to remove nodes in criminal networks by considering the new links generated by the removal. STONE [13] reshapes networks by predicting who replaces a "removed" individual in a terrorist network then identifying which k people to remove to minimize the organization's efficiency (formalized in different ways).…”

Section: Related Workmentioning

confidence: 99%

SPINN: Suspicion prediction in nuclear networks

Andrews

Kumar

Spezzano

et al. 2015

2015 IEEE International Conference on Intelligence and Security Informatics (ISI)

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The best known analyses to date of nuclear proliferation networks are qualitative analyses of networks consisting of just hundreds of nodes and edges. We propose SPINNa computational framework that performs the following tasks. Starting from existing lists of sanctioned entities, SPINN automatically builds a highly augmented network by scraping connections between individuals, companies, and government organizations from sources like LinkedIN and public company data from Bloomberg.By analyzing this open source information alone, we have built up a network of over 74K nodes and 1.09M edges, containing a smaller whitelist and a blacklist. We develop numerous "features" of nodes in such networks that take both intrinsic node properties and network properties into account, and based on these, we develop methods to classify previously unclassified nodes as suspicious or unsuspicious. On 10-fold cross validation on ground truth data, we obtain a Matthews Correlation Coefficient for our best classifier of just over 0.9. We show that of the 10 most relevant features for distinguishing between suspicious and non-suspicious nodes, the top 8 are network related measures including a novel notion of suspicion rank.

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Section: Related Workmentioning

confidence: 99%

SPINN: Suspicion prediction in nuclear networks

Andrews

Kumar

Spezzano

et al. 2015

2015 IEEE International Conference on Intelligence and Security Informatics (ISI)

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“…Previous software designed to support law-enforcement through social network analysis include CrimeFighter [14] and CrimeLink [15]. However, these tools provide complementary capabilities to ORCA.…”

Section: Related Workmentioning

confidence: 99%

Social network intelligence analysis to combat street gang violence

Paulo

Fischl

Markow

et al. 2013

Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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In this paper we introduce the Organization, Relationship, and Contact Analyzer (ORCA) that is designed to aide intelligence analysis for law enforcement operations against violent street gangs. ORCA is designed to address several police analytical needs concerning street gangs using new techniques in social network analysis. Specifically, it can determine "degree of membership" for individuals who do not admit to membership in a street gang, quickly identify sets of influential individuals (under the tipping model), and identify criminal ecosystems by decomposing gangs into sub-groups. We describe this software and the design decisions considered in building an intelligence analysis tool created specifically for countering violent street gangs as well as provide results based on conducting analysis on real-world police data provided by a major American metropolitan police department who is partnering with us and currently deploying this system for real-world use.

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“…Finally, a structural parser assists the investigators by relating otherwise unrelated information in different ways, either based on the entities themselves or by applying algorithms to analyze them. In the following, core CrimeFighter Investigator features are presented, but unfortunately space limitations mean that details are limited and not all supported features can be presented (see [13,14,[25][26][27] for more details).…”

Section: Crimefighter Investigatormentioning

confidence: 99%

“…Customized combinations of different algorithms can be created for frequent use. For example, we developed a custom node removal algorithm which can be used by criminal network investigators to ask 'what-if' questions about the secondary effects of removing a key individual from a criminal network [25].…”

Section: Sense-making Supportmentioning

confidence: 99%

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CrimeFighter Investigator: Integrating synthesis and sense-making for criminal network investigation

Petersen

Wiil

2013

Secur Inform

Self Cite

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Criminal network investigation involves a number of complex tasks and problems. Overall tasks include collection, processing, and analysis of information, in which analysis is the key to successful use of information since it transforms raw data into intelligence. Analysts have to deal with problems such as information volume and complexity which are typically resolved with more resources. This approach together with sequential thinking introduces compartmentalization, inhibits information sharing, and ultimately results in intelligence failure. We view analysis as an iterative and incremental process of creative synthesis and logic-based sense-making where all stakeholders participate and contribute. This paper presents a novel tool that supports a human-centered, target-centric model for criminal network investigation. The developed tool provides more comprehensive support for analysis tasks than existing tools and measures of performance indicate that the integration of synthesis and sense-making is feasible.

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Node Removal in Criminal Networks

Cited by 20 publications

References 7 publications

SPINN: Suspicion prediction in nuclear networks

SPINN: Suspicion prediction in nuclear networks

Social network intelligence analysis to combat street gang violence

CrimeFighter Investigator: Integrating synthesis and sense-making for criminal network investigation

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