A census-based genetic algorithm for target set selection problem in social networks

• Published in: Neural computing & applications Vol. 37; no. 28; pp. 23155 - 23183
• Main Authors: Rahman, Md. Samiur, Ahsan, Mohammad Shamim, Chen, Cheng-Wu, Varadarajan, Vijayakumar
• Format: Journal Article
• Language: English
• Published: London Springer London 01.10.2025; Springer Nature B.V
• Subjects: Apexes; Artificial Intelligence; Census; Censuses; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Dynamic programming; Genetic algorithms; Graph theory; Graphs; Heuristic; Image Processing and Computer Vision; Original Article; Probability and Statistics in Computer Science; Social networks; Self-adaptive genetic algorithm; Target set selection problem; Census-based genetic algorithm
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-025-11480-3

This paper considers the target set selection (TSS) problem in social networks, a fundamental problem in viral marketing. In the TSS problem, a graph and a threshold value for each vertex of the graph is given. A minimum size vertex subset needs to be found to “activate” such that all graph vertices are activated at the end of the propagation process. Specifically, we propose a novel approach called “a census-based genetic algorithm” for the TSS problem. In our algorithm, the concept of a census is utilized to gather and store information about individuals in a population and collect census data from the individuals constructed during the algorithm’s execution so that greater diversity and avoiding premature convergence can be achieved at locally optimal solutions. Specifically, two distinct census informations have been used: (a) For individuals, the algorithm stores how many times it has been identified during the execution; (b) for each network node, the algorithm counts how many times it has been included in a solution. The proposed algorithm can also be self-adjusted by using a parameter specifying the aggressiveness employed in each reproduction method. Additionally, the algorithm is designed to run in a parallelized environment to minimize the computational cost and check individual’s feasibility. Moreover, our algorithm finds the optimal solution in all cases while experimenting on random graphs. Furthermore, the proposed algorithm has been employed on 14 large graphs of real-life social network instances from the literature, improving around 9.57 solution size (on average) and 134 vertices (in total) compared to the best solutions obtained in previous studies.