Information Cascades in Professional Networks: A Graph-Based Study of LinkedIn Post Engagement

Information cascades in professional networks represent a critical mechanism for knowledge transfer and career development, yet their dynamics remain poorly understood. This study presents a comprehensive empirical analysis of information cascades in LinkedIn professional networks, focusing on computer science professionals and academic-industry knowledge transfer. We analysed 50,000 CS professionals, 500,000 connections, and 100,000 technical posts over 12 months using a Modified Independent Cascade Model that incorporates professional context factors. Our analysis reveals that hybrid professionals, representing only 25% of the network, account for 52% of inter-cluster connections and achieve 2.8× higher cross-domain transfer rates. Educational content demonstrates superior cross-domain appeal (0.47) compared to research papers (0.23), with optimal posting windows between 10 AM-12 PM achieving 23% higher cross-domain engagement. Bridge users in academic-industry transitions show significantly higher transfer effectiveness (Cohen's d = 1.47, p < 0.001). These findings provide evidence-based strategies for optimising professional networking and knowledge dissemination across academic and industry domains