mlsanityData to 7 Oct 2026

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

arXiv 2308.07883 ↗First posted 15 Aug 2023cs.LG cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 23 of its 31 references that are in the map. First posted on arXiv 15 Aug 2023.

Builds on

23 of 31 references.

References · 23

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2018How Powerful are Graph Neural Networks?cited by 4,355
  5. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,133
  6. 2020Temporal Graph Networks for Deep Learning on Dynamic Graphscited by 521
  7. 2020Inductive Representation Learning on Temporal Graphscited by 454
  8. 2019EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphscited by 443
  9. 2019Predicting Dynamic Embedding Trajectory in Temporal Interaction Networkscited by 443
  10. 2020SIGN: Scalable Inception Graph Neural Networkscited by 283
  11. 2019Representation Learning for Dynamic Graphs: A Surveycited by 260
  12. 2018Modeling polypharmacy side effects with graph convolutional networkscited by 237
11 more
  1. 2023Do We Really Need Complicated Model Architectures For Temporal Networks?cited by 141
  2. 2023Temporal Graph Benchmark for Machine Learning on Temporal Graphscited by 137
  3. 2020Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A surveycited by 123
  4. 2022Towards Better Evaluation for Dynamic Link Predictioncited by 120
  5. 2019Fake News Detection on Social Media using Geometric Deep Learningcited by 115
  6. 2022Provably expressive temporal graph networkscited by 56
  7. 2020Edge-variational Graph Convolutional Networks for Uncertainty-aware Disease Predictioncited by 25
  8. 2019E-LSTM-D: A Deep Learning Framework for Dynamic Network Link Predictioncited by 24
  9. 2018Learning Dynamic Embeddings from Temporal Interactionscited by 10
  10. 2019DyANE: Dynamics-aware node embedding for temporal networkscited by 7
  11. 2022Representation Learning in Continuous-Time Dynamic Signed Networkscited by 6

Built on by

Not cited yet.