  Scientific impact has been the center of extended debate regarding its
accuracy and reliability. From hiring committees in academic institutions to
governmental agencies that distribute funding, an author's scientific success
as measured by the h-index is a vital point to their career. The objective of
this work is to investigate whether the collaboration patterns of an author are
good predictors of the author's future $h$-index. Although not directly related
to each other, a more intense collaboration can result into increased
productivity which can potentially have an impact on the author's future
$h$-index. In this paper, we capitalize on recent advances in graph neural
networks and we examine the possibility of predicting the $h$-index relying
solely on the author's collaboration and the textual content of a subset of
their papers. We perform our experiments on a large-scale network consisting of
more than $1$ million authors that have published papers in computer science
venues and more than $37$ million edges. The task is a six-months-ahead
forecast, i.e. what the $h$-index of each author will be after six months. Our
experiments indicate that there is indeed some relationship between the future
$h$-index of an author and their structural role in the co-authorship network.
Furthermore, we found that the proposed method outperforms standard machine
learning techniques based on simple graph metrics along with node
representations learned from the textual content of the author's papers.
