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Data were analyzed by intent to treat. We use data from a large-scale mobile communication network built from billions of voice calls and short messaging events approximating interaction patterns at a societal scale. The framework offers a new, flexible way to explain the interplay between individual user actions and network distributions, and can benefit many applications. To date, network science has ignored the question of whether the small world phenomenon manifests itself in similar ways across dyadic classes defined by individual traits, such as age or sex.
At the macro-level, we try to understand how network distributions such as power-law or heavy-tailed phenomena can be explained by group behavior. Tencent Weibo, Citation, and Flickr. At the micro-level, we seek to capture the way in which an individual user decides whether to perform an action.
At the meso-level, we study how group behavior develops and evolves over time, based on individual actions. We also use information-burst prediction as a particular application to quantitatively evaluate the predictive power of the proposed framework.
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