Reconstructing supply networks

被引:0
|
作者
Mungo, Luca [1 ,2 ,3 ]
Brintrup, Alexandra [4 ]
Garlaschelli, Diego [5 ,6 ]
Lafond, Francois [1 ,7 ]
机构
[1] Univ Oxford, Oxford Martin Sch, Inst New Econ Thinking, Oxford, England
[2] Univ Oxford, Math Inst, Oxford, England
[3] Macrocosm Inc, New York, NY USA
[4] Univ Cambridge, Inst Mfg, Dept Engn, Cambridge, England
[5] IMT Sch Adv Studies, Lucca, Italy
[6] Leiden Univ, Lorentz Inst Theoret Phys, Leiden, Netherlands
[7] Univ Oxford, Smith Sch Enterprise & Environm, Oxford, England
来源
JOURNAL OF PHYSICS-COMPLEXITY | 2024年 / 5卷 / 01期
关键词
link prediction; supply networks; value chains; maximum entropy; machine learning; COMPLEX-SYSTEMS; PROPAGATION; INFORMATION; SHOCKS;
D O I
10.1088/2632-072X/ad30bf
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
Network reconstruction is a well-developed sub-field of network science, but it has only recently been applied to production networks, where nodes are firms and edges represent customer-supplier relationships. We review the literature that has flourished to infer the topology of these networks by partial, aggregate, or indirect observation of the data. We discuss why this is an important endeavour, what needs to be reconstructed, what makes it different from other network reconstruction problems, and how different researchers have approached the problem. We conclude with a research agenda.
引用
收藏
页数:21
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