mlsanityData to 14 Oct 2026

SupplyGraph: A Benchmark Dataset for Supply Chain Planning using Graph Neural Networks

arXiv 2401.15299 ↗First posted 27 Jan 2024cs.LG cs.AI cs.IRcited by 9

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Abstract (arXiv)

Graph Neural Networks (GNNs) have gained traction across different domains such as transportation, bio-informatics, language processing, and computer vision. However, there is a noticeable absence of research on applying GNNs to supply chain networks. Supply chain networks are inherently graph-like in structure, making them prime candidates for applying GNN methodologies. This opens up a world of possibilities for optimizing, predicting, and solving even the most complex supply chain problems. A major setback in this approach lies in the absence of real-world benchmark datasets to facilitate the research and resolution of supply chain problems using GNNs. To address the issue, we present a real-world benchmark dataset for temporal tasks, obtained from one of the leading FMCG companies in Bangladesh, focusing on supply chain planning for production purposes. The dataset includes temporal data as node features to enable sales predictions, production planning, and the identification of factory issues. By utilizing this dataset, researchers can employ GNNs to address numerous supply chain problems, thereby advancing the field of supply chain analytics and planning. Source: https://github.com/CIOL-SUST/SupplyGraph

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 12 of its 40 references that are in the map. First posted on arXiv 27 Jan 2024.

Builds on

12 of 40 references.

References · 12

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  3. 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,367
  4. 2018Link Prediction Based on Graph Neural Networkscited by 832
  5. 2021Graph Neural Network-Based Anomaly Detection in Multivariate Time Seriescited by 301
  6. 2016Structured Sequence Modeling with Graph Convolutional Recurrent Networkscited by 275
  7. 2017A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencodercited by 209
  8. 2021End-to-End Constrained Optimization Learning: A Surveycited by 157
  9. 2021PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Modelscited by 118
  10. 2020Line Graph Neural Networks for Link Predictioncited by 89
  11. 2021Data Considerations in Graph Representation Learning for Supply Chain Networkscited by 10
  12. 2020Simultaneous Decision Making for Stochastic Multi-echelon Inventory Optimization with Deep Neural Networks as Decision Makerscited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 3; 2026: 4.

9 papers.

  1. 2025C-MAG: Cascade Multimodal Attributed Graphs for Supply Chain Link Predictioncited by 3
  2. 2024Learning production functions for supply chains with graph neural networkscited by 2
  3. 2024Applying graph neural network to SupplyGraph for supply chain networkcited by 2
  4. 2025Optimizing Supply Chain Networks with the Power of Graph Neural Networkscited by 1
  5. 2025SynDelay: A Synthetic Dataset for Delivery Delay Predictioncited by 0
  6. 2026From Topology to Trajectory: LLM-Driven World Models For Supply Chain Resiliencecited by 0
  7. 2026ISOMORPH: A Supply Chain Digital Twin for Simulation, Dataset Generation, and Forecasting Benchmarkscited by 0
  8. 2026ReflectiChain: Epistemic Grounding in LLM-Driven World Models for Supply Chain Resiliencecited by 0
  9. 2026On the Potential of Graph Neural Networks as Metamodels for Supply Chain Optimization: Dataset, Architectures, and Directionscited by 0