Abstract
Online auctions has become the main frauds source and demolishing the trust to the traders in this well accepted and soaring transaction records internet commerce model. The anonymity of internet transaction in online auction market creates the difficulty for identity verification. Traditional demographic-data-based verifications are no longer valid in Internet auction.. The online auction reputation inflation frauds are utilizing the low cost of multiple identities to build inflated reputation for deceiving. The interactions among these reputation inflated accounts are essential to fabricate the reputation in the online auction market. To detect such premeditated online auction frauds, the effected indicators have to exploit the nature of network community structures of these reputation inflated accounts. To reduce the information inequilibrium or to balance the advantage caused by the information inequilibrium, previous researches have focused on the approach of verifying the genuineness of the information or revealing its value. This research focused on the balancing the information processing capability to unveil the trader who manipulated the reputation. Based on the cumulated magnitude of online auction transaction data, the trader's transaction social network can be characterized as detection indicator of reputation inflated traders. By gathering the actual online auction transaction data from eBay Inc. American and the methodology from social network analysis (SNA), this research are able to construct the transactional network structure measurements to discriminate the reputation inflated traders from regular accounts. The power of these two measurements was verified by two approaches, by statistical significance testing and the accuracy contribution in the supervised neural network model.