Resilience Inference for Supply Chains with Hypergraph Neural Network

• URL: http://arxiv.org/abs/2511.06208v1
• Date: Sun, 09 Nov 2025 03:34:45 GMT
• Title: Resilience Inference for Supply Chains with Hypergraph Neural Network
• Authors: Zetian Shen, Hongjun Wang, Jiyuan Chen, Xuan Song,
• Abstract summary: Supply chains are integral to global economic stability, yet disruptions can propagate through interconnected networks, resulting in substantial economic impacts.<n> Accurate and timely inference of supply chain resilience the capability to maintain core functions during disruptions is crucial for proactive risk mitigation and robust network design.<n>Existing approaches lack effective mechanisms to infer supply chain resilience without explicit system dynamics and struggle to represent the higher-order, multi-entity inherent in supply chain networks.
• Score: 10.029608878220973
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Supply chains are integral to global economic stability, yet disruptions can swiftly propagate through interconnected networks, resulting in substantial economic impacts. Accurate and timely inference of supply chain resilience the capability to maintain core functions during disruptions is crucial for proactive risk mitigation and robust network design. However, existing approaches lack effective mechanisms to infer supply chain resilience without explicit system dynamics and struggle to represent the higher-order, multi-entity dependencies inherent in supply chain networks. These limitations motivate the definition of a novel problem and the development of targeted modeling solutions. To address these challenges, we formalize a novel problem: Supply Chain Resilience Inference (SCRI), defined as predicting supply chain resilience using hypergraph topology and observed inventory trajectories without explicit dynamic equations. To solve this problem, we propose the Supply Chain Resilience Inference Hypergraph Network (SC-RIHN), a novel hypergraph-based model leveraging set-based encoding and hypergraph message passing to capture multi-party firm-product interactions. Comprehensive experiments demonstrate that SC-RIHN significantly outperforms traditional MLP, representative graph neural network variants, and ResInf baselines across synthetic benchmarks, underscoring its potential for practical, early-warning risk assessment in complex supply chain systems.

Related papers

• Quantum-Inspired Reinforcement Learning for Secure and Sustainable AIoT-Driven Supply Chain Systems [28.316261067681555]
  This work integrates a quantum-inspired reinforcement learning framework that unifies carbon footprint reduction, inventory management, and cryptographic-like security measures.<n>In simulation, the method exhibits smooth convergence, strong late-episode performance, and graceful degradation under representative noise channels.<n>These findings reinforce the potential for quantum-inspired AIoT frameworks to drive secure, eco-conscious supply chain operations at scale.
  arXiv  Detail & Related papers  (2026-01-29T21:33:08Z)
• PowerGrow: Feasible Co-Growth of Structures and Dynamics for Power Grid Synthesis [75.14189839277928]
  We present PowerGrow, a co-generative framework that significantly reduces computational overhead while maintaining operational validity.<n> Experiments across benchmark settings show that PowerGrow outperforms prior diffusion models in fidelity and diversity.<n>This demonstrates its ability to generate operationally valid and realistic power grid scenarios.
  arXiv  Detail & Related papers  (2025-08-29T01:47:27Z)
• An Analytics-Driven Approach to Enhancing Supply Chain Visibility with Graph Neural Networks and Federated Learning [52.79646338275159]
  We propose a novel approach that integrates Federated Learning (FL) and Graph Convolutional Neural Networks (GCNs) to enhance supply chain visibility.<n>FL enables collaborative model training across countries by facilitating information sharing without requiring raw data exchange.<n>GCNs empower the framework to capture intricate relational patterns within knowledge graphs, enabling accurate link prediction to uncover hidden connections.
  arXiv  Detail & Related papers  (2025-03-10T12:15:45Z)
• AI Flow at the Network Edge [58.31090055138711]
  AI Flow is a framework that streamlines the inference process by jointly leveraging the heterogeneous resources available across devices, edge nodes, and cloud servers.<n>This article serves as a position paper for identifying the motivation, challenges, and principles of AI Flow.
  arXiv  Detail & Related papers  (2024-11-19T12:51:17Z)
• TDNetGen: Empowering Complex Network Resilience Prediction with Generative Augmentation of Topology and Dynamics [14.25304439234864]
  We introduce a novel resilience prediction framework for complex networks, designed to tackle this issue through generative data augmentation of network topology and dynamics. Experiment results on three network datasets demonstrate that our proposed framework TDNetGen can achieve high prediction accuracy up to 85%-95%.
  arXiv  Detail & Related papers  (2024-08-19T09:20:31Z)
• Enhancing Supply Chain Visibility with Knowledge Graphs and Large Language Models [49.898152180805454]
  This paper presents a novel framework leveraging Knowledge Graphs (KGs) and Large Language Models (LLMs) to enhance supply chain visibility. Our zero-shot, LLM-driven approach automates the extraction of supply chain information from diverse public sources. With high accuracy in NER and RE tasks, it provides an effective tool for understanding complex, multi-tiered supply networks.
  arXiv  Detail & Related papers  (2024-08-05T17:11:29Z)
• Disentangling the Causes of Plasticity Loss in Neural Networks [55.23250269007988]
  We show that loss of plasticity can be decomposed into multiple independent mechanisms. We show that a combination of layer normalization and weight decay is highly effective at maintaining plasticity in a variety of synthetic nonstationary learning tasks.
  arXiv  Detail & Related papers  (2024-02-29T00:02:33Z)
• HKTGNN: Hierarchical Knowledge Transferable Graph Neural Network-based Supply Chain Risk Assessment [3.439495194421287]
  We propose a hierarchical knowledge transferable graph neural network-based (HKTGNN) supply chain risk assessment model. We embed the supply chain network corresponding to individual goods in the supply chain using the graph embedding module. Our model outperforms in experiments on a real-world supply chain dataset.
  arXiv  Detail & Related papers  (2023-11-07T00:54:04Z)
• Leveraging Low-Rank and Sparse Recurrent Connectivity for Robust Closed-Loop Control [63.310780486820796]
  We show how a parameterization of recurrent connectivity influences robustness in closed-loop settings. We find that closed-form continuous-time neural networks (CfCs) with fewer parameters can outperform their full-rank, fully-connected counterparts.
  arXiv  Detail & Related papers  (2023-10-05T21:44:18Z)
• QAmplifyNet: Pushing the Boundaries of Supply Chain Backorder Prediction Using Interpretable Hybrid Quantum-Classical Neural Network [1.227497305546707]
  Supply chain management relies on accurate backorder prediction for optimizing inventory control, reducing costs, and enhancing customer satisfaction. This research introduces a novel methodological framework for supply chain backorder prediction, addressing the challenge of handling large datasets. Our proposed model, QAmplifyNet, employs quantum-inspired techniques within a quantum-classical neural network to predict backorders effectively on short and imbalanced datasets.
  arXiv  Detail & Related papers  (2023-07-24T15:59:36Z)

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.