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

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...

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Bibliographic Details
Main Authors: Zaynab Azizpour, Saeid Taghavi Afshord, Bagher Zarei, Mohammad Ali Jabraeil Jamali, Shahin Akbarpour
Format: Article
Language:English
Published: Ferdowsi University of Mashhad 2025-05-01
Series:Computer and Knowledge Engineering
Subjects:
social networks
influence maximization
manta-ray foraging optimization algorithm
centrality criteria
Online Access:https://cke.um.ac.ir/article_46989_7597f89765eb96549452b4e4984f62a0.pdf
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Summary: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.
ISSN:2538-5453
2717-4123

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