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

Towards Temporal Edge Regression: A Case Study on Agriculture Trade Between Nations