  Centrality of a node measures its relative importance within a network. There
are a number of applications of centrality, including inferring the influence
or success of an individual in a social network, and the resulting social
network dynamics. While we can compute the centrality of any node in a given
network snapshot, a number of applications are also interested in knowing the
potential importance of an individual in the future. However, current
centrality is not necessarily an effective predictor of future centrality.
While there are different measures of centrality, we focus on degree centrality
in this paper. We develop a method that reconciles preferential attachment and
triadic closure to capture a node's prominence profile. We show that the
proposed node prominence profile method is an effective predictor of degree
centrality. Notably, our analysis reveals that individuals in the early stage
of evolution display a distinctive and robust signature in degree centrality
trend, adequately predicted by their prominence profile. We evaluate our work
across four real-world social networks. Our findings have important
implications for the applications that require prediction of a node's future
degree centrality, as well as the study of social network dynamics.
