TY  - JOUR
AU  - Sun, Peiyao
AU  - Zhang, Jingwen
AU  - Wang, Sizhe
AU  - Wang, Haohua
PY  - 2026
TI  - Identification of key nodes in complex networks via redundancy-aware maximal clique centrality
JO  - Natural Science of Hainan University
SN  - 1004-1729
SP  - 458
EP  - 470
VL  - 44
IS  - 4
AB  - 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.
UR  - https://doi.org/10.65658/j.hndk.2026031901
DO  - 10.65658/j.hndk.2026031901