document: Mining knowledge-sharing sites for viral marketing Viral marketing takes advantage of networks of influence among customers to inexpensively achieve large changes in behavior . Our research seeks to put it on a firmer footing by mining these networks from data , building probabilistic models of them , and using these models to choose the best viral marketing plan . Knowledge-sharing sites , where customers review products and advise each other , are a fertile source for this type of data mining . In this paper we extend our previous techniques , achieving a large reduction in computational cost , and apply them to data from a knowledge-sharing site . We optimize the amount of marketing funds spent on each customer , rather than just making a binary decision on whether to market to him . We take into account the fact that knowledge of the network is partial , and that gathering that knowledge can itself have a cost . Our results show the robustness and utility of our approach .
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