NDLI: SPINN: Suspicion prediction in nuclear networks

Content Provider	IEEE Xplore Digital Library
Author	Andrews, I.A. Kumar, S. Spezzano, F. Subrahmanian, V.S.
Copyright Year	2015
Description	Author affiliation: Sch. of Public Policy, Univ. of Maryland, College Park, MD, USA (Andrews, I.A.) \|\| Comput. Sci. Dept., Univ. of Maryland, College Park, MD, USA (Kumar, S.; Spezzano, F.; Subrahmanian, V.S.)
Abstract	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 SPINN - a 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.
Starting Page	19
Ending Page	24
File Size	931835
Page Count	6
File Format	PDF
e-ISBN	9781479998890
DOI	10.1109/ISI.2015.7165933
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-05-27
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines LinkedIn Correlation Companies Standards
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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