  Recently, Graph Neural Networks (GNNs) have shown promising performance in
tasks on dynamic graphs such as node classification, link prediction and graph
regression. However, few work has studied the temporal edge regression task
which has important real-world applications. In this paper, we explore the
application of GNNs to edge regression tasks in both static and dynamic
settings, focusing on predicting food and agriculture trade values between
nations. We introduce three simple yet strong baselines and comprehensively
evaluate one static and three dynamic GNN models using the UN Trade dataset.
Our experimental results reveal that the baselines exhibit remarkably strong
performance across various settings, highlighting the inadequacy of existing
GNNs. We also find that TGN outperforms other GNN models, suggesting TGN is a
more appropriate choice for edge regression tasks. Moreover, we note that the
proportion of negative edges in the training samples significantly affects the
test performance. The companion source code can be found at:
https://github.com/scylj1/GNN_Edge_Regression.
