A Method to Predict Semantic Relations on Artificial Intelligence Papers
This work addresses the challenge of forecasting research trends in AI, but it is incremental as it applies an existing deep learning method to a specific competition dataset.
The paper tackled the problem of predicting future links in a large network of AI concepts, as posed by the Science4cast competition, and presented a Graph Neural Network-based solution that achieved competitive results despite computational restrictions.
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.