Graph-based machine learning models have gained significant attention in predicting the emergence of new relationships in evolving networks. In this work, we present a study on forecasting scientific collaborations using a Graph Attention Network (GAT) with L2 regularization and dropout. We construct yearly co-authorship graphs based on historical publication data and analyze the evolution of these graphs over time with the International Conference on Computational Science (ICCS) as an example of a living scientific community. Our approach involves training on past yearly graphs to predict the formation of new edges in future graphs. We assess the model’s performance by varying the prediction window and evaluating results using link prediction metrics. The proposed method demonstrates the feasibility of utilizing deep learning techniques for predicting future collaborations based on past scientific interactions.