Detecting hidden communities in online auction networks

Kai Zhu, Yong Guan, Lei Ying

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication2012 46th Annual Conference on Information Sciences and Systems, CISS 2012
DOIs
StatePublished - Nov 12 2012
Event2012 46th Annual Conference on Information Sciences and Systems, CISS 2012 - Princeton, NJ, United States
Duration: Mar 21 2012Mar 23 2012

Publication series

Name2012 46th Annual Conference on Information Sciences and Systems, CISS 2012

Other

Other2012 46th Annual Conference on Information Sciences and Systems, CISS 2012
CountryUnited States
CityPrinceton, NJ
Period3/21/123/23/12

ASJC Scopus subject areas

  • Information Systems

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    Zhu, K., Guan, Y., & Ying, L. (2012). Detecting hidden communities in online auction networks. In 2012 46th Annual Conference on Information Sciences and Systems, CISS 2012 [6310907] (2012 46th Annual Conference on Information Sciences and Systems, CISS 2012). https://doi.org/10.1109/CISS.2012.6310907