@article{Sun2026, 
author = {Peiyao Sun and Jingwen Zhang and Sizhe Wang and Haohua Wang},
title = {Identification of key nodes in complex networks via redundancy-aware maximal clique centrality},
year = {2026},
journal = {Natural Science of Hainan University},
volume = {44},
number = {4},
pages = {458-470},
keywords = {redundancy-aware, maximal clique, complex networks, key node identification},
url = {https://www.sciopen.com/article/10.65658/j.hndk.2026031901},
doi = {10.65658/j.hndk.2026031901},
abstract = {To address the issue of influence overlap commonly caused by existing methods in multi-source spreading on complex networks, this paper proposes a redundancy-aware maximal clique centrality method for key node identification in complex networks. Taking maximal cliques as the fundamental computational unit, the proposed method accurately quantifies the higher-order bridging value of nodes as they span low-constraint maximal cliques. Furthermore, Katz centrality is introduced as a global penalty term to identify topologically dispersed and complementary sub-core hubs, thereby achieving dynamical de-redundancy. Parameter analysis experiments reveal the intrinsic correlation between de-redundancy intensity and network structure. Susceptible-Infected-Recovered spreading dynamics simulations conducted on six real-world networks demonstrate that this method effectively overcomes local spreading interference, achieving the optimal standardized steady-state infection scale across various intervention ratios. Numerical results, supported by analyses of the monotonicity index and average shortest path length, confirm that the proposed method exhibits exceptionally high node-ranking resolution and spatial dispersion.}
}