How seed construction impacts the influence spread in social networks

Influence spread remains one of the most extensively studied phenomena in social network analysis due to its relevance for understanding real-world problems. It is well known that an isolated node can hardly exert significant influence, while overly large initial seeds lead to trivially high spread. This paper analyzes which characteristics initial seeds should have — when constructed from a given actor — in order to produce diverse and well-differentiated influence spread outcomes across actors, thereby yielding meaningful centrality criteria. To this end, we conduct controlled experiments using the General Influence Spread Rank (GISR), a centrality measure that evaluates the influence spread capacity of a node together with selected neighbors under specific parameter settings. The experiments consider 27 networks with varying levels of connectivity under 96 different parameter configurations. The results, based on correlation analysis and growth curve evaluation, highlight the importance of both neighborhood depth and initial activation probability, depending on network topology and the underlying diffusion model.