Modeling Dynamical Influence in Human Interaction Patterns
Computing Research Repository(2010)
摘要
How can we model influence between individuals in a social system, even when
the network of interactions is unknown? In this article, we review the
literature on the "influence model," which utilizes independent time series to
estimate how much the state of one actor affects the state of another actor in
the system. We extend this model to incorporate dynamical parameters that allow
us to infer how influence changes over time, and we provide three examples of
how this model can be applied to simulated and real data. The results show that
the model can recover known estimates of influence, it generates results that
are consistent with other measures of social networks, and it allows us to
uncover important shifts in the way states may be transmitted between actors at
different points in time.
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