  We report on a model built to predict links in a complex network of
scientific concepts, in the context of the Science4Cast 2021 competition. We
show that the network heavily favours linking nodes of high degree, indicating
that new scientific connections are primarily made between popular concepts,
which constitutes the main feature of our model. Besides this notion of
popularity, we use a measure of similarity between nodes quantified by a
normalized count of their common neighbours to improve the model. Finally, we
show that the model can be further improved by considering a time-weighted
adjacency matrix with both older and newer links having higher impact in the
predictions, representing rooted concepts and state of the art research,
respectively.
