influence maximization in social networks using discrete manta-ray foraging optimization algorithm and combination of centrality criteria

Influence maximization (im) is a fundamental problem in social network analysis that seeks to identify a small set of highly influential nodes that can maximize the spread of information. due to its np-hard nature, finding an exact solution is computationally infeasible for large-scale networks. to address this, this paper introduces an enhanced discrete manta-ray foraging optimization (mrfo) algorithm tailored for im. the proposed method integrates degree, closeness, and betweenness centrality measures into the fitness function and introduces a fused centrality index to improve the identification of influential nodes. to handle the discrete search space, the continuous mrfo is adapted with novel discretization mechanisms. experimental evaluations on five real-world networks (netscience, email, hamsterster, ego-facebook, and pages-publicfigure) demonstrate that the proposed method achieves higher influence spread compared to existing baseline algorithms, with average improvements of 14.63%, 12.81%, 19.03%, 15.24%, and 18.76%, respectively. these results validate the effectiveness, robustness, and practical applicability of the proposed approach for large-scale im.