  Understanding the evolution of paper and author citations is of paramount
importance for the design of research policies and evaluation criteria that can
promote and accelerate scientific discoveries. Recently many studies on the
evolution of science have been conducted in the context of the emergent science
of science field. While many studies have probed the link problem in citation
networks, only a few works have analyzed the temporal nature of link prediction
in author citation networks. In this study we compared the performance of 10
well-known local network similarity measurements to predict future links in
author citations networks. Differently from traditional link prediction
methods, the temporal nature of the predict links is relevant for our approach.
Our analysis revealed interesting results. The Jaccard coefficient was found to
be among the most relevant measurements. The preferential attachment
measurement, conversely, displayed the worst performance. We also found that
the extension of local measurements to their weighted version do not
significantly improved the performance of predicting citations. Finally, we
also found that a neural network approach summarizing the information from all
10 considered similarity measurements was not able to provide the highest
prediction performance.
