Influence Maximization in Social Networks Using Discrete Manta-Ray Foraging Optimization Algorithm and Combination of Centrality Criteria

Document Type : Original Article

Authors

Zaynab Azizpour

Saeid Taghavi Afshord

Bagher Zarei

Mohammad Ali Jabraeil Jamali

Shahin Akbarpour

Department of Computer Engineering, Shab.C., Islamic Azad University, Shabestar, Iran

10.22067/cke.2025.91839.1148

Abstract

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.

Keywords

Social networks

Influence maximization

Manta-Ray Foraging optimization algorithm

Centrality criteria

Subjects

• Computer Science

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