How does Social Network Influence Job Search on Career-oriented Social Platforms? A Study Based on the Multi-agent Simulation Method

[Purpose]: This study aims to explore the underlying mechanisms and dynamic evolutionary processes of career-oriented social platforms, particularly focusing on how specific social network concepts like "closure" and "connection" affect job seekers’ social capital and job search efficiency. [Design/methodology/approach]: The research employs a multi-agent simulation system to analyze the effects of triadic closure, focal closure, membership closure, and third-degree of influence (THDI) within social networks. LinkedIn data is utilized to simulate and investigate these mechanisms. [Findings]: The simulation results indicate that triadic closure, focal closure, and THDI positively influence the social capital of job seekers. This increase in social capital is driven by activities like establishing new interpersonal connections, which in turn enhances network evolution efficiency. Additionally, membership closure impacts job seekers’ willingness to apply for positions, improving job search efficiency. The THDI mechanism plays a key role in facilitating network evolution, while the alumni function, influenced by focal closure, improves talent-market. [Originality/value]: This study uncovers the dynamic evolutionary rules in social networks and validates the pivotal role of career-oriented social platforms in enhancing employment efficiency. The findings provide actionable insights for optimizing platform operations, ultimately facilitating more efficient job searching and recruiting activities.