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.

本文提出了一种基于 Graph Neural Networks 的新型深度学习方法，以解决如何预测大型演化网络中链接出现的问题，特别地，预测与 AI 相关的主题之间的链接。实验结果表明，使用此方法能够有效地识别出节点的吸收和密集子图的合并两种模式，且模型具有较高的预测准确性。