全球供应链网络的蝴蝶结结构与社区识别
Bow-tie structure and community identification of global supply chain network
摘要：
本文从全球供应链网络的度分布、层次结构和度-度相关性等方面研究了全球供应链网络的拓扑性质。全球供应链数据是通过收集2018年标准普尔资本智商平台网站上的各种公司数据构建的。入度和出度分布的特征在于幂律，入度指数= 2.42，出度指数= 2.11。聚类系数呈幂律衰减，指数为0.46。节点度-度相关性表明不存在重复性。GWCC的蝴蝶结结构表明，OUT成分最大，占总公司的41.1%。GSCC占公司总数的16.4%。我们观察到，在上游或下游侧的企业大多位于几步之遥的GSCC。此外，我们揭示了网络的社区结构，并根据它们的位置和行业分类来描述它们。我们观察到，最大的社区由主要位于美国的非必需消费品行业组成。这些公司属于全球供应链网络蝴蝶结结构中的OUT部分。最后，我们确认命题S1（短路径长度），S2（幂律度分布），S3（高聚集系数），S4（“适合更富有”的增长机制），S5（截断幂律度分布），和S7（社区结构与重叠边界）在全球供应链网络的有效性。
英文摘要：
We study on topological properties of global supply chain network in terms of degree distribution, hierarchical structure, and degree-degree correlation in the global supply chain network. The global supply chain data is constructed by collecting various company data from the web site of Standard & Poor's Capital IQ platform in 2018. The in- and out-degree distributions are characterized by a power law with in-degree exponent = 2.42 and out-degree exponent = 2.11. The clustering coefficient decays as power law with an exponent = 0.46. The nodal degree-degree correlation indicates the absence of assortativity. The Bow-tie structure of GWCC reveals that the OUT component is the largest and it consists 41.1% of total firms. The GSCC component comprises 16.4% of total firms. We observe that the firms in the upstream or downstream sides are mostly located a few steps away from the GSCC. Furthermore, we uncover the community structure of the network and characterize them according to their location and industry classification. We observe that the largest community consists of consumer discretionary sector mainly based in the US. These firms belong to the OUT component in the bow-tie structure of the global supply chain network. Finally, we confirm the validity for propositions S1 (short path length), S2 (power-law degree distribution), S3 (high clustering coefficient), S4 ("fit-gets-richer" growth mechanism), S5 (truncation of power-law degree distribution), and S7 (community structure with overlapping boundaries) in the global supply chain network.
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