Mesoscopic structural holes for redundancy-aware node ranking in complex networks

Traditional structural hole theory characterizes non-redundant bridging positions by evaluating redundancy from pairwise neighbor relationships, yet it often neglects cohesive local group structures. We propose Mesoscopic Structural Holes (MSH), a redundancy-aware node-ranking method that treats maximal cliques as mesoscopic units in pairwise graphs and ranks nodes through two dimensions: effective dependence on affiliated cliques and inter-clique redundancy. Grounded in resource dependence theory, MSH reformulates structural constraints from a mesoscopic perspective and favors nodes occupying less redundant bridging positions across cohesive groups. Evaluations on nine real-world networks against eleven baseline methods show that MSH maintains high ranking resolution, while its top-ranked node sets generally exhibit lower structural redundancy and competitive collective SIR spreading performance. Correlation and ablation studies further support MSH's structural distinctiveness and the roles of its main components. Efficiency experiments on synthetic networks indicate that MSH is computationally feasible for sparse to moderately dense networks, with runtime primarily determined by maximal-clique enumeration and clique-structure complexity.