Wortman, Jennifer
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Publication Viral Marketing and the Diffusion of Trends on Social Networks(2008-05-15) Wortman, JenniferWe survey the recent literature on theoretical models of diffusion in social networks and the application of these models to viral marketing. To put this work in context, we begin with a review of the most common models that have been examined in the economics and sociology literature, including local interaction games, threshold models, and cascade models, in addition to a family of models based on Markov random fields. We then discuss a series of recent algorithmic and analytical results that have emerged from the computer science community. The first set of results addresses the problem of influence maximization, in which the goal is to determine the optimal group of individuals in a social network to target with an advertising campaign in order to cause a new product or technology to spread throughout the network. We then discuss an analysis of the properties of graphs that allow or prohibit the widespread propagation of trends.Publication Learning from Multiple Sources(2008-06-01) Crammer, Koby; Kearns, Michael; Wortman, JenniferWe consider the problem of learning accurate models from multiple sources of "nearby" data. Given distinct samples from multiple data sources and estimates of the dissimilarities between these sources, we provide a general theory of which samples should be used to learn models for each source. This theory is applicable in a broad decision-theoretic learning framework, and yields general results for classification and regression. A key component of our approach is the development of approximate triangle inequalities for expected loss, which may be of independent interest. We discuss the related problem of learning parameters of a distribution from multiple data sources. Finally, we illustrate our theory through a series of synthetic simulations.