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The recent availability of massive amounts of networked data generated by email, instant messaging, mobile phone communications, micro blogs, and online social networks is enabling studies of population-level human interaction on scales orders of magnitude greater than what was previously possible.1'2 One important goal of applying statistical inference techniques to large networked datasets is to understand how behavioral contagions spread in human social networks. More precisely, understanding how people influence or are influenced by their peers can help us understand the ebb and flow of market trends, product adoption and diffusion, the spread of health behaviors such as smoking and exercise, the productivity of information workers, and whether particular individuals in a social network have a disproportion ate amount of influence on the system.


This is a pre-copy-editing, author-produced PDF of an article accepted for publication in IEEE Intelligent Systems, volume 26, issue 5, in 2011 following peer review. This article may not exactly replicate the final published version. The definitive publisher-authenticated version is available online at

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