Online auctions are gaining tremendous popularity in recent
years. Although providing unprecedent opportunities, online auction si-
tes become an attractive environment for fraud, theft, and deception.
Participants of online auctions agree that trustworthy reputation sys-
tems are an important factor in fighting dishonest and malicious users.
Unfortunately, popular auction sites use only very simple reputation es-
timation schemes that utilize feedbacks issued reciprocally by users after
terminated auctions. Such systems can be easily deceived and do not of-
fer sufficient protection against organized fraud. In this paper we present
a novel density-based reputation measure. The new reputation measure
uses the topology of seller-buyer connections to derive knowledge about
trustworthy sellers. We mine the data on past transactions to discover
clusters of connected sellers and for each seller we measure the density of
the seller's neighborhood. We perform many experiments on the body of
real-world data acquired from a leading Polish provider of online auctions
to examine the new measure in detail.