Analysis of the influence of organizational network structure on information flow and decision-making behavior based on graph algorithm

As the carrier of information flow and decision-making behavior, the form and characteristics of organizational network structure have a far-reaching impact on organizational operation. Graph algorithm, especially community discovery algorithm combined with centrality analysis, can reveal the membership relationship, information flow path and decision-making behavior pattern within the organization. Taking a large multinational company as an example, this paper uses Louvain algorithm to divide communities, and calculates the intensity and frequency of information flow between communities. It is found that the information flow in closely connected communities is smoother and the decision-making process is more efficient. Centrality analysis identifies key nodes, which play an important role in information dissemination and decision-making. The results show that the organizational network structure has a significant impact on information flow and decision-making behavior, and closely connected communities are more conducive to rapid information dissemination and efficient decision-making. The reasons for the differences in information flow and decision-making behavior under different network structures may include organizational culture, management system and technical platform. This paper provides theoretical support and practical guidance for optimizing organizational network structure, improving information flow efficiency and decision-making quality.