Building and Analyzing of Enterprise Network: A Case Study on China Automobile Supply Network

Liqiang Wang, Shijun Liu, Li Pan, Lei Wu and Xiangxu Meng
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Liqiang Wang: School of Computer Science and Technology, Shandong University, Jinan, China
Shijun Liu: Key Laboratory of Shandong Province for Software Engineering, School of Computer Science and Technology, Shandong University, Jinan, China
Li Pan: School of Computer Science and Technology, Shandong University, Jinan, China
Lei Wu: School of Computer Science and Technology, Shandong University, Jinan, China
Xiangxu Meng: Engineering Research Center of Digital Media Technology, Ministry of Education, School of Computer Science and Technology, Shandong University, Jinan, China

International Journal of Web Services Research (IJWSR), 2016, vol. 13, issue 3, 64-87

Abstract: Social business moves beyond linear, process-driven organizations to create new, dynamic, networked businesses that focus on customer value. Enterprise network (EN) is used to support social business by maximizing current and future opportunities and facilitate network-enabled processes, which can lead to value co-creation. EN is a multi-level hypergraph model with enterprises, employees, products and other related entities. In this paper the authors refine the EN model and present the foundation of EN to support social businesses. Then they introduce a case study on China automobile supply network (CASN). For the similarity with social networks, they verify power-law and small world theories in EN with statistical results on this data set. These theories are fitful in EN, but some new characteristics exist. The structure of EN consists of star-shaped clusters and the authors extract ego networks taking suppliers and manufacturers as the ego respectively. With the structure and distribution features of EN, they present the enterprise business similarity analysis method based on common-neighbors. And they also introduce the tentative work to detect Dunbar circles in EN. To analyze the data in a more intuitional and effective way, the authors use some data visualization tools to process the data in EN.

Date: 2016
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