{"id":607830,"date":"2019-09-09T09:04:00","date_gmt":"2019-09-09T16:04:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=607830"},"modified":"2019-09-09T09:04:26","modified_gmt":"2019-09-09T16:04:26","slug":"the-structure-of-online-diffusion-networks-2","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/the-structure-of-online-diffusion-networks-2\/","title":{"rendered":"The Structure of Online Diffusion Networks"},"content":{"rendered":"
Models of networked diffusion that are motivated by analogy with the spread of infectious disease have been applied to a wide range of social and economic adoption processes, including those related to new products, ideas, norms and behaviors. However, it is unknown how accurately these models account for the empirical structure of diffusion over networks. Here we describe the diffusion patterns arising from seven online domains, ranging from communications platforms to networked games to microblogging services, each involving distinct types of content and modes of sharing. We find strikingly similar patterns across all domains.
\nIn particular, the vast majority of cascades are small, and are described by a handful of simple tree structures that terminate within one degree of an initial adopting “seed.” In addition we find that structures other than these account for only a tiny fraction of total adoptions; that is, adoptions resulting from chains of referrals are extremely rare. Finally, even for the largest cascades that we observe, we find that the bulk of adoptions often takes place within one degree of a few dominant individuals. Together, these observations suggest new directions for modeling of online adoption processes.<\/p>\n","protected":false},"excerpt":{"rendered":"
Models of networked diffusion that are motivated by analogy with the spread of infectious disease have been applied to a wide range of social and economic adoption processes, including those related to new products, ideas, norms and behaviors. However, it is unknown how accurately these models account for the empirical structure of diffusion over networks. 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