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

TL;DR

This paper investigates the application of Graph Neural Networks to predict trade values between nations, revealing the strengths of simple baselines and the TGN model, and highlighting the impact of negative edge proportions on performance.

Contribution

It introduces strong baseline models for temporal edge regression and evaluates GNNs on trade data, demonstrating TGN's superior performance and the importance of negative edge ratios.

Findings

Baselines perform remarkably well across settings.

TGN outperforms other GNN models.

Negative edge proportion significantly affects test performance.

Abstract

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.