  Predicting the emergence of links in large evolving networks is a difficult
task with many practical applications. Recently, the Science4cast competition
has illustrated this challenge presenting a network of 64.000 AI concepts and
asking the participants to predict which topics are going to be researched
together in the future. In this paper, we present a solution to this problem
based on a new family of deep learning approaches, namely Graph Neural
Networks. The results of the challenge show that our solution is competitive
even if we had to impose severe restrictions to obtain a computationally
efficient and parsimonious model: ignoring the intrinsic dynamics of the graph
and using only a small subset of the nodes surrounding a target link.
Preliminary experiments presented in this paper suggest the model is learning
two related, but different patterns: the absorption of a node by a sub-graph
and union of more dense sub-graphs. The model seems to excel at recognizing the
first type of pattern.
