Two step graph-based semi-supervised learning for online auction fraud detection

Abstract

We analyze a social graph of online auction users and propose an online auction fraud detection approach. In this paper, fraudsters are those who participate in their own auction in order to drive up the final price. They tend to frequently bid in auctions hosted by fraudulent sellers, who work in the same collusion group. Our graph-based semi-supervised learning approach for online auction fraud detection is based on this social interaction of fraudsters. Auction users and their transactions are represented as a social interaction graph. Given a small set of known fraudsters, our aim was to detect more fraudsters based on the hypothesis that strong edges between fraudsters frequently exist in online auction social graphs. Detecting fraudsters who work in collusion with known fraudsters was our primary goal. We also found that weighted degree centrality is a distinct feature that separates fraudsters and legitimate users. We actively used this fact to detect fraud. To this end, we extended the modified adsorption model by incorporating the weighted degree centrality of nodes. The results, from real world data, show that by integrating the weighted degree centrality to the model can significantly improve accuracy.