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Online auctions have revolutionized the ability of people to buy and sell items without middlemen, and sales reaching more than $57 billion every year on eBay alone. The user interactions at online auctions form a network of interactions akin to a social network. Unlike other online social networks, online auction networks have not been studied so far. In this paper, we model and characterize the structure and evolution of the network of user interactions on eBay. Specifically, we use over 54 million eBay transaction feedback that users leave for each other. A distinguishing feature of the graph is the rich meaning that nodes and edges can have (for example, an edge could be positive or negative, posted by a buyer or a seller) in contrast to other graphs such as the topology of the Internet. First, we provide the vital statistics of the emerging graph, which we call eBay graph. We observe that the graph exhibits both significant differences and similarities to commonly studied graphs. Second, we study the feedback behavior of users: feedback is not always reciprocal, and negative feedback is scarce (less than 1%), but few negative feedbacks could discourage new users. Finally, we develop an intuitive model that captures key properties of the graph in a visual and memorable way. Our work can be seen as a first step in understanding online auction graphs, which could enable us to detect trends, abnormalities and ultimately fraudulent behavior.