Corporate Influence Analysis by Integrating Social Network Models
Konferenz: CIBDA 2022 - 3rd International Conference on Computer Information and Big Data Applications
25.03.2022 - 27.03.2022 in Wuhan, China
Tagungsband: CIBDA 2022
Seiten: 10Sprache: EnglischTyp: PDF
Autoren:
Wu, Tao (The University of Sydney, Sydney, Australia)
Inhalt:
This study uses a social network model to analyze the influence of firms in the market from multiple perspectives. Corporate ownership relationships, the volume of securities traded, and the internationalization distribution of firms will be explored as three influential factors in this paper. Data visualization of the network using Gephi reveals that there is a multipolar sub-network of core firms in the corporate ownership network, and the sub-network has a star-shaped topology. In contrast, the securities trading network and the internationalization distribution network show an aggregated distribution of core firms, with most of the core firms clustered in the center of the total network, and have a de-extending influence on the edge nodes. The ‘networkx’ package in Python is used to analyze the centrality of the three different social networks, and it is found that Betweenness Centrality does not have practical significance in all networks, while Eigenvector Centrality, compared with Degree Centrality, varies much less in the stock exchange network than in the other two networks. Vary much less than the other two network models.