Performance Evaluation of Evolutionary Algorithms on Solving the Influence Maximization Problem in Social Networks

Performance Evaluation of Evolutionary Algorithms on Solving the Influence Maximization Problem in Social Networks

Автор: Agash Uthayasuriyan, Hema Chandran G., Kavvin UV, Sabbineni Hema Mahitha, Jeyakumar G.

Журнал: International Journal of Modern Education and Computer Science @ijmecs

Статья в выпуске: 2 vol.16, 2024 года.

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Influence Maximization (IM) is an optimization problem that deals with identifying the most valuable individuals/ nodes present in the network to attain the maximal information spread when they are activated. Evolutionary Algorithms (EAs) inspired from nature are one of the most powerful methods to solve an optimization problem. This paper attempts to solve the IM problem using few of the popular EAs such as Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Differential Evolution (DE). These algorithm’s performance is evaluated using four comparative metrics, that deal with assessing solution quality, computational efficiency, and scalability. The experimental results of these EAs when tested on several real-world networks reveal that the GE and PSO algorithms were found to produce relatively poorer quality of seed nodes and also with higher computational costs, making it less preferrable. DE was able to obtain the best seed sets (in terms of influence spread value) than other algorithms and ACO, the fastest among all the considered algorithms in terms of execution time, was found to obtain seed set with appreciable influence spread with a slightly higher computational cost than DE.

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Social network, Influence Maximization, Seed nodes, Evolutionary Algorithm, Genetic Algorithm, Ant Colony Optimization, Particle Swarm Optimization, Differential Evolution

Короткий адрес: https://sciup.org/15019163

IDR: 15019163   |   DOI: 10.5815/ijmecs.2024.02.07

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