Multi-temporal heterogeneous graph learning with pattern-aware attention for industrial chain risk detection-Reference-Cited by-同舟云学术

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Multi-temporal heterogeneous graph learning with pattern-aware attention for industrial chain risk detection

Published:2024-06-15 Issue:4 Volume:27 Page:
ISSN:1386-145X
Container-title:World Wide Web
language:en
Short-container-title:World Wide Web

Author:

Li Ziheng,Sun Yongjiao,Bi Xin,Wang Ruijin,Ying Shi,Ji Hangxu

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Basic and Applied Basic Research Foundation of Guangdong Province

Postdoctoral Research Foundation of Northeastern University

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11280-024-01280-5.pdf

Reference39 articles.

1. Yang, M., Lim, M.K., Qu, Y., Ni, D., Xiao, Z.: Supply chain risk management with machine learning technology: A literature review and future research directions. Comput. Ind. Eng. 175, 108859 (2023)

2. Schroeder, M., Lodemann, S.: A systematic investigation of the integration of machine learning into supply chain risk management. Logistics 5(3), 62 (2021)

3. Kosasih, E.E., Margaroli, F., Gelli, S., Aziz, A., Wildgoose, N., Brintrup, A.: Towards knowledge graph reasoning for supply chain risk management using graph neural networks. Int. J. Prod. Res. 1–17 (2022)

4. Bernstein, F., Song, J.-S., Zheng, X.: Free riding in a multi-channel supply chain. Nav. Res. Logist. (NRL) 56(8), 745–765 (2009)

5. Yan, R.: Managing channel coordination in a multi-channel manufacturer-retailer supply chain. Ind. Mark. Manag. 40(4), 636–642 (2011)

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