Risk discovery based on recommendation flow analysis on social networks

Authors:
Jason J. Jung;Geun-Sik Jo
Affiliations:
Intelligent E-Commerce Systems Laboratory, Department of Computer and Information Engineering, Inha University, Incheon, Korea;Intelligent E-Commerce Systems Laboratory, Department of Computer and Information Engineering, Inha University, Incheon, Korea
Venue:
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Year:
2007

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Abstract

Social networks have been working as a medium to provide cooperative interactions between people. However, as some of users take malicious actions, the social network potentially contains some risks (e.g., information distortion). In this paper, we propose a robust information diffusion (or propagation) model to detect malicious peers on social network. Especially, we apply statistical sequence analysis to discover a peculiar patterns on recommendation flows. Through two experimentation, we evaluated the performance of risk discovery on social network.