Using Social Network Analysis to Study the Knowledge Sharing Mechanism of Start-Ups

Currently, the operation mode and influencing factors of knowledge sharing mechanism in start-ups have not been fully revealed, which leads to inefficient knowledge flow and thus affects enterprise innovation and development. To solve this problem, this paper uses social network analysis (SNA) to conduct an in-depth study on the knowledge sharing mechanism of start-ups. By collecting employees’ interaction data, a social network model of internal knowledge sharing in the enterprise is constructed, and the key knowledge transfer nodes are identified using node centrality analysis. The efficiency of knowledge flow is then analyzed through network density and small-world effect, and the potential bottlenecks of knowledge flow are revealed by combining community detection algorithms. The experimental results show that after optimizing the network structure, the efficiency of knowledge transfer within the enterprise is significantly improved; the network density reaches 0.74; the frequency of knowledge sharing within the community is increased by 25% through dynamic monitoring and continuous optimization. The knowledge sharing strategy based on network optimization proposed in this paper provides theoretical support and practical guidance for start-ups to improve the efficiency of knowledge flow and promote innovation.