Centrality Prediction in Dynamic Human Contact Networks
Real technological, social and biological networks evolve over time. Predicting their future topology has applications to epidemiology, targeted marketing, network reliability and routing in ad-hoc and peer-to-peer networks. The key problem for such applications is usually to identify the nodes that will be in more important positions in the future. Previous researchers had used ad-hoc prediction functions. In this paper, the authors evaluate ways of predicting a node's future importance under three important metrics, namely degree, closeness, and betweenness centrality, using empirical data on human contact networks collected using mobile devices. They find that node importance is highly predictable due to both periodic and legacy effects of human social behaviour, and they design reasonable prediction functions.