Leader-driven social network reconstruction

Understanding how opinion leader characteristics influence network reconstruction represents a critical challenge in computational social science. This study presents a novel framework integrating leader-driven opinion dynamics with compressive sensing, systematically investigating how node centrality, initial opinion, acceptance rate, and opinion homogeneity affect reconstruction accuracy. The extensive experimental results for three real-world networks and three synthetic models show that leaders with lower centrality consistently outperform highly central nodes in network reconstruction. This occurs because high centrality leaders create rapid opinion convergence, reducing the informational diversity essential for accurate reconstruction, while lower centrality leaders preserve richer signal content. Our analysis shows that extremely conservative leaders (o=0.0) with high stubbornness (α=1.0) achieve optimal performance in moderately tolerant communities (ε=0.5), challenging conventional centrality-based leader selection strategies. These findings indicate that effective opinion leadership for network reconstruction requires consideration of dynamics-specific factors beyond traditional structural importance, with significant implications for marketing, public health interventions, and crisis communication applications.