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Proceedings Paper

The pearls of using real-world evidence to discover social groups
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Paper Abstract

In previous work, we introduced a new paradigm called Uni-Party Data Community Generation (UDCG) and a new methodology to discover social groups (a.k.a., community models) called Link Discovery based on Correlation Analysis (LDCA). We further advanced this work by experimenting with a corpus of evidence obtained from a Ponzi scheme investigation. That work identified several UDCG algorithms, developed what we called "Importance Measures" to compare the accuracy of the algorithms based on ground truth, and presented a Concept of Operations (CONOPS) that criminal investigators could use to discover social groups. However, that work used a rather small random sample of manually edited documents because the evidence contained far too many OCR and other extraction errors. Deferring the evidence extraction errors allowed us to continue experimenting with UDCG algorithms, but only used a small fraction of the available evidence. In attempt to discover techniques that are more practical in the near-term, our most recent work focuses on being able to use an entire corpus of real-world evidence to discover social groups. This paper discusses the complications of extracting evidence, suggests a method of performing name resolution, presents a new UDCG algorithm, and discusses our future direction in this area.

Paper Details

Date Published: 28 March 2005
PDF: 12 pages
Proc. SPIE 5812, Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2005, (28 March 2005); doi: 10.1117/12.603763
Show Author Affiliations
Raymond A. Cardillo, Air Force Research Lab. (United States)
John J. Salerno, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 5812:
Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2005
Belur V. Dasarathy, Editor(s)

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