  The degree to which individuals can exert influence on propagation of
information and opinion dynamics in online communities is highly dependent on
their social status. Therefore, there is a high demand for identifying
influential users in a community by predicting their social position in that
community. Moreover, understanding how people with various social status
behave, can shed light on the dynamics of interaction in social networks. In
this paper, I study an evolving online social network originated from an online
community for university students and I tackle the problem of forecasting
users' social status, represented as their PageRank, based on frequency of
recurring temporal sequences of observed behavior, i.e. behavioral motifs. I
show that individuals with different values of PageRank exhibit different
behavior even in early weeks since the online community's inception and it is
possible to forecast future PageRank values given frequency of behavioral
motifs with high accuracy.
