On the Potential of Graph Neural Networks as Metamodels for Supply Chain Optimization: Dataset, Architectures, and Directions

• URL: http://arxiv.org/abs/2607.16769v1
• Date: Sat, 18 Jul 2026 11:06:22 GMT
• Title: On the Potential of Graph Neural Networks as Metamodels for Supply Chain Optimization: Dataset, Architectures, and Directions
• Abstract summary: Graph Neural Networks (GNNs) have emerged as a powerful, differentiable class of learning models forstructured systems.<n>This paper lays the foundation, presents initial steps, and discusses key research directions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph Neural Networks (GNNs) have emerged as a powerful, differentiable class of learning models for graph-structured systems. Their ability to generalize across topologies opens the prospect of a surrogate for combined structural and parametric optimization, which classical metamodels cannot offer. Supply chains are a natural target, yet the use of GNN surrogates for supply chain problems is largely unexplored. This paper lays the foundation, presents initial steps, and discusses key research directions. As a foundation, we formulate the problem and create a large public training dataset of programmatically generated supply chain graphs with input parameters and steady-state performance metrics obtained using our SupplyNetPy simulation library. As initial steps, we explore GNN architectures that work well as surrogates for node- and network-level predictions, and analyze their accuracy-compute trade-off against simulation. Most importantly, we outline the exciting directions this opens, namely gradient-based optimization over topology, fast design-space exploration, and sensitivity analysis.

Related papers

• A Roadmap for Applying Graph Neural Networks to Numerical Data: Insights from Cementitious Materials [5.565428903960444]
  This work is among the first few studies to implement Graph neural network (GNN) models to design concrete.<n>GNN is capable of learning from data structured as graphs, capturing relationships through irregular or topology-dependent connections.<n>The proposed framework establishes a strong foundation for future multi-modal and physics-informed GNN models.
  arXiv  Detail & Related papers  (2025-12-16T19:17:05Z)
• On the Impact of Graph Neural Networks in Recommender Systems: A Topological Perspective [49.391877616394765]
  In recommender systems, user-item interactions can be modeled as a bipartite graph, where user and item nodes are connected by undirected edges.<n>This graph-based view has motivated the rapid adoption of graph neural networks (GNNs)<n>Despite their empirical success, the reasons why GNNs offer systematic advantages over other approaches remain only partially understood.
  arXiv  Detail & Related papers  (2025-12-08T10:19:43Z)
• Prefetching Cache Optimization Using Graph Neural Networks: A Modular Framework and Conceptual Analysis [0.0]
  This paper introduces a modular framework leveraging Graph Neural Networks (GNNs) to model and predict access patterns within graph-structured data.<n>We provide a detailed conceptual analysis showing how GNN-based approaches can outperform conventional methods by learning intricate dependencies.
  arXiv  Detail & Related papers  (2025-10-23T10:35:35Z)
• A Graph Laplacian Eigenvector-based Pre-training Method for Graph Neural Networks [7.359145401513628]
  Structure-based pre-training methods are under-explored yet crucial for downstream applications which rely on underlying graph structure.<n>We propose the Laplacian Eigenvector Learning Module (LELM), a novel pre-training module for graph neural networks (GNNs) based on predicting the low-frequency eigenvectors of the graph Laplacian.<n>LELM introduces a novel architecture that overcomes oversmoothing, allowing the GNN model to learn long-range interdependencies.
  arXiv  Detail & Related papers  (2025-09-02T20:07:20Z)
• Optimizing Supply Chain Networks with the Power of Graph Neural Networks [0.0]
  Graph Neural Networks (GNNs) have emerged as transformative tools for modeling complex relational data.<n>This study investigates the application of GNNs to demand forecasting within supply chain networks using the SupplyGraph dataset.
  arXiv  Detail & Related papers  (2025-01-07T02:31:24Z)
• Interpretable A-posteriori Error Indication for Graph Neural Network Surrogate Models [0.0]
  This work introduces an interpretability enhancement procedure for graph neural networks (GNNs) The end result is an interpretable GNN model that isolates regions in physical space, corresponding to sub-graphs, that are intrinsically linked to the forecasting task. The interpretable GNNs can also be used to identify, during inference, graph nodes that correspond to a majority of the anticipated forecasting error.
  arXiv  Detail & Related papers  (2023-11-13T18:37:07Z)
• GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networks [72.01829954658889]
  This paper introduces the mathematical definition of this novel problem setting. We devise a general framework that coordinates a single graph-shared structure learner and multiple graph-specific GNNs. The well-trained structure learner can directly produce adaptive structures for unseen target graphs without any fine-tuning.
  arXiv  Detail & Related papers  (2023-06-20T03:33:22Z)
• GNN at the Edge: Cost-Efficient Graph Neural Network Processing over Distributed Edge Servers [24.109721494781592]
  Graph Neural Networks (GNNs) are still under exploration, presenting a stark disparity to its broad edge adoptions. This paper studies the cost optimization for distributed GNN processing over a multi-tier heterogeneous edge network. We show that our approach achieves superior performance over de facto baselines with more than 95.8% cost eduction in a fast convergence speed.
  arXiv  Detail & Related papers  (2022-10-31T13:03:16Z)
• A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking [124.21408098724551]
  Large-scale graph training is a notoriously challenging problem for graph neural networks (GNNs) We present a new ensembling training manner, named EnGCN, to address the existing issues. Our proposed method has achieved new state-of-the-art (SOTA) performance on large-scale datasets.
  arXiv  Detail & Related papers  (2022-10-14T03:43:05Z)
• MentorGNN: Deriving Curriculum for Pre-Training GNNs [61.97574489259085]
  We propose an end-to-end model named MentorGNN that aims to supervise the pre-training process of GNNs across graphs. We shed new light on the problem of domain adaption on relational data (i.e., graphs) by deriving a natural and interpretable upper bound on the generalization error of the pre-trained GNNs.
  arXiv  Detail & Related papers  (2022-08-21T15:12:08Z)
• Optimal Propagation for Graph Neural Networks [51.08426265813481]
  We propose a bi-level optimization approach for learning the optimal graph structure. We also explore a low-rank approximation model for further reducing the time complexity.
  arXiv  Detail & Related papers  (2022-05-06T03:37:00Z)
• Data-Driven Learning of Geometric Scattering Networks [74.3283600072357]
  We propose a new graph neural network (GNN) module based on relaxations of recently proposed geometric scattering transforms. Our learnable geometric scattering (LEGS) module enables adaptive tuning of the wavelets to encourage band-pass features to emerge in learned representations.
  arXiv  Detail & Related papers  (2020-10-06T01:20:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.