FoodTrade: A Benchmark for Graph-Based Learning on Temporal Food Trade Networks

This work introduces FoodTrade, a dynamic temporal network dataset covering global trade in four staple grains from 1992 to 2023, comprehensively curated from multiple public sources, preprocessed, and ready-to-use for graph-based machine learning. The dataset presents a combination of challenges often encountered in machine learning, such as a noisy, low-sample, high-dimensional, and heavy-tailed structure with temporal distribution shifts and the presence of hidden confounders. With these characteristics, FoodTrade offers a new and difficult benchmark for graph-based learning at a time when the community is increasingly concerned about stagnated progress due to limitations of current benchmarks. We analyze the evolution of the link structure and the presence of network shocks, formulate predictive tasks at the node and edge levels, build an evaluation framework, and benchmark several baselines across different model classes. While temporal GNNs may seem like a natural choice for modeling the food trade network, they are not currently the best-performing model. Heuristics, econometric, and tabular ML methods often perform as well as or better than GNNs. Therefore, this dataset contributes to the call for more meaningful benchmarks that facilitate effective evaluation of graph-learning methods beyond simple solutions. We outline potential directions for better modeling of the food trade network and improving predictions of critical variables. Therefore, the dataset paves the way for developing specialized predictive tools that invite collaboration with domain experts to advance impactful data-driven AI solutions to assess global food security.