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Centrality of a node measures its relative importance within a network. Thereare a number of applications of centrality, including inferring the influenceor success of an individual in a social network, and the resulting socialnetwork dynamics. While we can compute the centrality of any node in a givennetwork snapshot, a number of applications are also interested in knowing thepotential importance of an individual in the future. However, currentcentrality is not necessarily an effective predictor of future centrality.While there are different measures of centrality, we focus on degree centralityin this paper. We develop a method that reconciles preferential attachment andtriadic closure to capture a node's prominence profile. We show that theproposed node prominence profile method is an effective predictor of degreecentrality. Notably, our analysis reveals that individuals in the early stageof evolution display a distinctive and robust signature in degree centralitytrend, adequately predicted by their prominence profile. We evaluate our workacross four real-world social networks. Our findings have importantimplications for the applications that require prediction of a node's futuredegree centrality, as well as the study of social network dynamics.

Predicting Node Degree Centrality with the Node Prominence Profile