Supply Chain Network Resilience Enhancement and Information Dissemination From the Perspective of Complex Network Theory

Qiang Zhou

International Journal of Intelligent Information Technologies2025https://doi.org/10.4018/ijiit.373202article
ABDC C
Weight
0.37

What the paper says

This paper analyzes the resilience enhancement and information dissemination of supply chain networks from the perspective of complex network theory. First, a supply chain topology model based on complex networks is constructed, and the key nodes and vulnerable links in the supply chain are identified using network structure characteristics such as node centrality and connectivity. Then, the risk resistance of the supply chain network is quantitatively evaluated using node failure simulation and network connectivity evaluation, and an optimization strategy based on the application of redundant nodes and network reconstruction is proposed to enhance the supply chain resilience under external shocks.

1 citation

Open paper page →

Cite this paper

https://doi.org/https://doi.org/10.4018/ijiit.373202

Or copy a formatted citation

@article{qiang2025,
  title        = {{Supply Chain Network Resilience Enhancement and Information Dissemination From the Perspective of Complex Network Theory}},
  author       = {Qiang Zhou},
  journal      = {International Journal of Intelligent Information Technologies},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.4018/ijiit.373202},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Supply Chain Network Resilience Enhancement and Information Dissemination From the Perspective of Complex Network Theory

Flags are reviewed by the Arbiter methodology team within 5 business days.


Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.