Empirical topological investigation of practical supply chains based on complex networks

Liao, Hao; Shen, Jing; Wu, Xingtong; Chen, Bokui; Zhou, Mingyang

doi:10.1088/1674-1056/26/11/110505

Cited by 19 publications

(15 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Next, Perera et al (2017) and Bier et al (2020) introduce a comprehensive review of the methodologies. They describe how to build the topology and robustness of supply chain networks, and the suitability of small-world networks and scale-free networks in the complex networks (Chen & Lin, 2012;Hearnshaw & Wilson, 2013;Liao et al, 2017). Zhang (2014) and Siddique et al (2018) selected scale-free networks based on analysis of the evolution of the logistics distribution network and collaboration patterns in material handling, respectively.…”

Section: Literature Reviewmentioning

confidence: 99%

Network Interactions of Global Supply Chain Members

Oláh

Pakurár

2021

Journal of Business Economics and Management

View full text Add to dashboard Cite

Supply chain structure of global enterprises tend to develop dramatically. These lead to more difficulty for enterprises in managing and building information sharing systems. Thus, it is a necessary for enterprises to limit the scope of the information sharing system by selecting essential partners. The aims of this study are to quantify the cooperation of each supply chain member, and evaluate and visualize their effects in information sharing systems in order to support policymakers in making their decisions in supply chain management. The network analytical method in network science is applied to indicate the relationship between supply chain members and present a comprehensive supply chain visually. Moreover, Motor Corporation’s topology in Japan is used as a representation of global enterprise features to analyze the relationships between supply chain members. The data for Motor Corporation is secondary data which includes the number of suppliers, manufacturers, and dealers, and the interaction among them. Data is collected and verified from reputable websites such as www.marklines.com, or www.statista.com. As a result, this study contributes by applying a new method for not only determining the impact levels of supply chain members but also giving visual descriptions of impact levels on the large-scale information sharing system.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Network Interactions of Global Supply Chain Members

Oláh

Pakurár

2021

Journal of Business Economics and Management

View full text Add to dashboard Cite

show abstract

“…Parola et al [8] investigated inter-firm communities through network analysis. Liao et al [9] detected the community structure in practical supply chains based on complex networks, while [10] presented an efficient method to identify port communities. Ducruet and Zaidi [11] focused on the analysis of community structures, providing evidence about the role of geography, technology and trade in the maritime networks, and Ansorena [12] determined the community structure of the liner shipping network.…”

Section: Relevant Literaturementioning

confidence: 99%

Finding Competitors and Strategies in Maritime Transportation of Fruits and Vegetables in Spain

Alvarez¹,

Calzada-Infante²,

Adenso‐Díaz³

2021

WIT Transactions on the Built Environment

View full text Add to dashboard Cite

Complex network analysis (CNA) has appeared as a flexible tool to model systems in multiple fields. With the nodes representing the entities of the system, and the links the relationships among them, CNA allows a topological characterisation of the system, assessing its structure. In recent years, researchers have shown an interest in modelling maritime traffic as a complex network, for analysing connections and relationships among different stakeholders. This paper proposes to model the maritime traffic flow of some food products, traded internationally. The model considers the traffic flows from Spain, allowing to group the partner countries whose traffic flow patterns are similar. These groups (communities), can help decision makers to identify their direct competitors and search for alliances or new markets comparing themselves with those ports that have similar infrastructures. The abovementioned procedure is applied to the export traffic of fruit, vegetables and legumes. Spain is the main fruit and vegetable producer in the EU, the first exporter in the EU and the third in the world. In the Spanish fruit and vegetable sector, part of the products are exported to the EU partly by land, but Spanish ports play a critical role in the transport of this type of goods. The importance of exports to other countries, which are mainly carried out by sea, makes it particularly interesting to study the role of Spanish ports in the export of this type of goods, and the possibilities of improving maritime traffic of these cargo for the Spanish port system. The conclusions derived from the analysis of the export connectivity of Spanish ports for the goods studied are presented, with some of the most important ports for these goods being geographically distant from the production areas, but with a high land connectivity with these regions.

show abstract

“…With the vigorous growth of network and information technology represented by the Internet, human society has entered a new and complex era of networks. Information mining in complex networks is important in theoretical research and offers great application and socioeconomic values [1][2][3][4]. For example, if users can unearth important nodes or edges in the spread network of a virus, then they can curb the spread of the virus in a short time by isolating or cutting off the important nodes or edges in the virus network at the beginning of the virus spread and thereby eliminate unnecessary economic losses [5].…”

Section: Introductionmentioning

confidence: 99%

DDMF: A Method for Mining Relatively Important Nodes Based on Distance Distribution and Multi-Index Fusion

et al. 2022

View full text Add to dashboard Cite

In research on complex networks, mining relatively important nodes is a challenging and practical work. However, little research has been done on mining relatively important nodes in complex networks, and the existing relatively important node mining algorithms cannot take into account the indicators of both precision and applicability. Aiming at the scarcity of relatively important node mining algorithms and the limitations of existing algorithms, this paper proposes a relatively important node mining method based on distance distribution and multi-index fusion (DDMF). First, the distance distribution of each node is generated according to the shortest path between nodes in the network; then, the cosine similarity, Euclidean distance and relative entropy are fused, and the entropy weight method is used to calculate the weights of different indexes; Finally, by calculating the relative importance score of nodes in the network, the relatively important nodes are mined. Through verification and analysis on real network datasets in different fields, the results show that the DDMF method outperforms other relatively important node mining algorithms in precision, recall, and AUC value.

show abstract

Empirical topological investigation of practical supply chains based on complex networks

Cited by 19 publications

References 38 publications

Network Interactions of Global Supply Chain Members

Network Interactions of Global Supply Chain Members

Finding Competitors and Strategies in Maritime Transportation of Fruits and Vegetables in Spain

DDMF: A Method for Mining Relatively Important Nodes Based on Distance Distribution and Multi-Index Fusion

Contact Info

Product

Resources

About