Identifying influential users and predicting their ‘‘network impact’’ on social networks have attracted
tremendous interest from both academia and industry. Various definitions of ‘‘influence’’ and many
methods for calculating influence scores have been provided for different empirical purposes and they
often lack the in-depth analysis of the ‘‘characteristics’’ of the output influence. In addition, most of
the developed algorithms and tools are mainly dependent on the static network structure instead of
the dynamic diffusion process over the network, and are thus essentially based on descriptive models
instead of predictive models.