NAMSA: A Non-Dominated Archived Multi-Objective Simulated Annealing Algorithm for Detecting Influencers in Social Networks

Social networks have become an integral part of our daily lives, providing an effective platform for communication, sharing information, and social interaction. With the explosive growth of social networks, the study of influence maximization (IM) in social networks has gained significant attention. IM refers to the process of identifying a small set of individuals, called influencers, who can initiate a cascade of events leading to the maximum spread of influence in the network. This paper proposes a non-dominated archived multi-objective simulated annealing (NAMSA) algorithm for detecting influencers in social networks. Our proposed algorithm maintains an archive of non-dominated solutions and uses a new mechanism to generate new solutions. We compare the results of the NAMSA algorithm with six other state-of-the-art algorithms and show that the NAMSA algorithm outperforms them. Furthermore, we propose a dynamic NAMSA (DNAMSA) algorithm suited for dynamic social networks and show that it performs better than four existing algorithms. The time complexities of proposed NAMSA and DNAMSA are comparable with other algorithms considered for comparison, and all the results are statistically significant.