Comparing various algorithms for discovering social groups with uni-party data

John J. Salerno; Raymond A. Cardillo; Zhongfei Mark Zhang

doi:10.1117/12.603680

28 March 2005 Comparing various algorithms for discovering social groups with uni-party data

John J. Salerno, Raymond A. Cardillo, Zhongfei Mark Zhang

Proceedings Volume 5812, Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2005; (2005) https://doi.org/10.1117/12.603680
Event: Defense and Security, 2005, Orlando, Florida, United States

Abstract

The challenge of identifying important individuals and their membership as part of a group is a continuing and ever growing problem. In recent years, the data mining community has been identifying and discussing a new paradigm of data analysis using uni-party data. Within this paradigm, a methodology known as Link Discovery based on Correlation Analysis (LDCA), defines a process to compensate for the lack of relational data. CORAL, a specific implementation of LDCA, demonstrated the value of this methodology by identifying suspects involved in a Ponzi scheme with limited success. This paper introduces several new algorithms and analyzes their ability to generate a prioritized ranking of individuals involved in the Ponzi scheme based on their individual activity. To compare the accuracy of each algorithm, we present the experimental results of the algorithms, and conclude with a discussion of open issues and future activities.

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John J. Salerno, Raymond A. Cardillo, and Zhongfei Mark Zhang "Comparing various algorithms for discovering social groups with uni-party data", Proc. SPIE 5812, Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2005, (28 March 2005); https://doi.org/10.1117/12.603680

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