• Chinese Physics B
  • Vol. 29, Issue 8, (2020)
Ji-Wei Hu1、2, Song Gao1、2, Jun-Wei Yan1、2, Ping Lou1、2、†, and Yong Yin1
Author Affiliations
  • 1School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China
  • 2Hubei Key Laboratory of Broadband Wireless Communication and Sensor, Wuhan University of Technology, Wuhan 430070, China
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    DOI: 10.1088/1674-1056/ab96a8 Cite this Article
    Ji-Wei Hu, Song Gao, Jun-Wei Yan, Ping Lou, Yong Yin. Manufacturing enterprise collaboration network: An empirical research and evolutionary model[J]. Chinese Physics B, 2020, 29(8): Copy Citation Text show less
    Bipartite network structure.
    Fig. 1. Bipartite network structure.
    MECN visualization. Red nodes are OEMs and blue nodes are part suppliers.
    Fig. 2. MECN visualization. Red nodes are OEMs and blue nodes are part suppliers.
    MLE fit of degree distribution of (a) OEMs and (b) part suppliers.
    Fig. 3. MLE fit of degree distribution of (a) OEMs and (b) part suppliers.
    Degree correlation in one-mode projection network on (a) OEMs and (b) part suppliers.
    Fig. 4. Degree correlation in one-mode projection network on (a) OEMs and (b) part suppliers.
    MECN evolutionary model construction process.
    Fig. 5. MECN evolutionary model construction process.
    MECN evolutionary model visualization. Red nodes are OEMs and blue nodes are part suppliers.
    Fig. 6. MECN evolutionary model visualization. Red nodes are OEMs and blue nodes are part suppliers.
    Distributions of the evolutionary model (blue), compared with empirical network (red) and the degree-based model (green), showing (a) the degree distribution of OEMs, and (b) degree distribution of part suppliers.
    Fig. 7. Distributions of the evolutionary model (blue), compared with empirical network (red) and the degree-based model (green), showing (a) the degree distribution of OEMs, and (b) degree distribution of part suppliers.
    NoPart suppliersFound yearLocationRegistered capitalNumber of intellectual propertiesNumber of risk warnings
    1Guangzhou Qicheng Auto Parts Co., Ltd.2013Guangdong2.0 × 106 CNY16
    2Shenyang Pinghe Valeo Automotive Transmission System Co., Ltd.2013Liaoning3.5 × 107 USD164
    3Shanxi Zhongjin Industrial Machinery Co., Ltd.1996Shanxi8.0 × 106 CNY291
    4Conde Ryan electromagnetic technology (China) co., Ltd.2005Jiangsu9.8 × 106 EUR230
    5Shanghai saks powertrain components system co., Ltd.2001Shanghai1.4 × 107 USD894
    Table 1. Information about part suppliers (a portion).
    Topological propertiesResult
    OEMs245
    Part suppliers6790
    Edges20938
    Part suppliers per OEM85.46
    OEMs per part suppliers3.08
    Average path length L3.755
    Clustering coefficient c0.253
    Assortativity coefficient r−0.480
    (one-mode projection on OEMs)−0.231
    Assortativity coefficient rM
    (one-mode projection on part suppliers)−0.021
    Assortativity coefficient rS
    OEM degreeMax: 963 and Min: 1
    Part supplier degreeMax: 23 and Min: 1
    Table 2. Topological properties of empirical MECN.
    Distributionp(k)D (OEM)D (part supplier)
    Power lawkα0.21070.1309
    Truncated power lawkαeαk0.05450.0372
    Exponentialeλk0.32600.0301
    Stretched exponential(λk)β – 1e−(λk)β0.06490.0203
    Table 3. Degree distribution fitting results of empirical MECN.
    NotationMeaning
    ttime step of evolution
    u0,v0,e0initial number of OEM nodes, part supplier nodes, and edges respectively
    enumber of edges
    nM,nS,nEmaximum number of OEM nodes, part supplier nodes, and edges respectively
    ki,kjdegree of nodes i and j respectively
    fi,fjfitness of nodes i and j calculated by the entropy-TOPSIS method respectively
    dmax_s, dmax_mmaximum number of part supplier nodes that OEM nodes can connect with
    maximum number of OEM nodes that part supplier nodes can connect with
    Sijprobability of successful connection between nodes i and j
    qparameter that affects where the link is deleted (from local-world or global network)
    Qiprobability of being selected as local-world network for node i
    Table 4. Notations used in evolutionary model.
    Topological propertiesEmpirical networkEvolutionary modelDegree-based model
    OEMs245245245
    Part suppliers679067906790
    Edges209382093820938
    Part suppliers per OEM85.4685.4685.46
    OEMs per part supplier3.083.083.08
    Average path length L3.7553.760 ± 0.0153.854 ± 0.015
    Clustering coefficient c0.2530.224 ± 0.0020.219 ± 0.02
    Assortativity coefficient r−0.480−0.417 ± 0.030−0.534 ± 0.040
    Assortativity coefficient rM−0.231−0.265 ± 0.020−0.203 ± 0.020
    (one-mode projection on OEMs)
    Assortativity coefficient rS−0.021−0.044 ± 0.015−0.034 ± 0.015
    (one-mode projection on part suppliers)
    OEM degreeMax: 963 Min: 1Max: 949 ± 1 Min: 1Max: 600 ± 150 Min: 1
    Part supplier degreeMax: 23 Min: 1Max: 22 ± 2 Min: 1Max: 21 ± 2 Min: 1
    Table 5. Results of topological properties.
    Topological propertiesEvolutionary modelDegree-based model
    Average path length L0.0210.099
    Clustering coefficient c0.0290.034
    Average degree 00
    Assortativity coefficient r0.0660.073
    Assortativity coefficient rM0.0360.032
    (one-mode projection on OEMs)
    Assortativity coefficient rS0.0360.032
    (one-mode projection on part suppliers)
    Table 6. RMSD results of topological properties.
    Ji-Wei Hu, Song Gao, Jun-Wei Yan, Ping Lou, Yong Yin. Manufacturing enterprise collaboration network: An empirical research and evolutionary model[J]. Chinese Physics B, 2020, 29(8):
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