DocumentCode :
3437595
Title :
Detecting hidden communities in online auction networks
Author :
Zhu, Kai ; Guan, Yong ; Ying, Lei
Author_Institution :
Dept. of Electr. & Comput. Eng., Iowa State Univ., Ames, IA, USA
fYear :
2012
fDate :
21-23 March 2012
Firstpage :
1
Lastpage :
6
Abstract :
Online auction networks often use reputation-based systems to help users assess each other´s honesty and integrity. Fraudsters, however, can collude with accomplices to accumulate bogus positive feedback to manipulate the reputation systems. In this paper, we model an online auction network with fraudsters as a random network with hidden communities (fraudsters and associated accomplices), and propose a maximum likelihood framework to detect the fraudsters. We develop an iterative message passing algorithm to heuristically solve the maximum likelihood detection problem. This algorithm identifies fraudsters and accomplices in a distributed fashion and is a scalable solution. The algorithm converges in a finite number of iterations and has very high detection rates according to our simulations.
Keywords :
electronic commerce; fraud; iterative methods; maximum likelihood estimation; message passing; bogus positive feedback accumulation; detection rate; fraudster detection; hidden community detection; iterative message passing algorithm; maximum likelihood detection problem; online auction networks; random network; reputation system manipulation; reputation-based systems;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Sciences and Systems (CISS), 2012 46th Annual Conference on
Conference_Location :
Princeton, NJ
Print_ISBN :
978-1-4673-3139-5
Electronic_ISBN :
978-1-4673-3138-8
Type :
conf
DOI :
10.1109/CISS.2012.6310907
Filename :
6310907
Link To Document :
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