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原文連結
論文資訊
- 類型：已發表論文
- 日期：2023-01-31
摘要
The vulnerability of supply chains and their role in the propagation of shocks has been highlighted multiple times in recent years, including by the recent pandemic. However, while the importance of micro data is increasingly recognised, data at the firm-to-firm level remains scarcely available. In this study, we formulate supply chain 網絡s' re-construction as a link prediction problem and tackle it using 機器學習, specifically Gradient Boosting. We test our approach on three different supply chain datasets and show that it works very well and outperforms three benchmarks. An analysis of features' importance suggests that the key data underlying our predictions are firms' industry, location, and size. To evaluate the feasibility of reconstructing a 網絡 when no production 網絡 data is available, we
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