  It is well-known that many networks follow a power-law degree distribution;
however, the factors that influence the formation of their distributions are
still unclear. How can one model the connection between individual actions and
network distributions? How can one explain the formation of group phenomena and
their evolutionary patterns?
  In this paper, we propose a unified framework, M3D, to model human dynamics
in social networks from three perspectives: macro, meso, and micro. 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. 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. We provide
theoretical analysis for the proposed framework, and discuss the connection of
our framework with existing work.
  The framework offers a new, flexible way to explain the interplay between
individual user actions and network distributions, and can benefit many
applications. To model heavy-tailed distributions from partially observed
individual actions and to predict the formation of group behaviors, we apply
M3D to three different genres of networks: Tencent Weibo, Citation, and Flickr.
We also use information-burst prediction as a particular application to
quantitatively evaluate the predictive power of the proposed framework. Our
results on the Weibo indicate that M3D's prediction performance exceeds that of
several alternative methods by up to 30\%.
