A non-dominated archived multi-objective harmony search algorithm for identifying influencers in social networks

Abstract Social media plays an important role in today’s life. The dissemination of messages and the exchange of information within social networks have been extensively researched and analyzed. In social media, influence maximization (IM) is the problem of finding a small subset of the most influential nodes and maximizing influence over the entire network by their combined influence-spreading capability. We propose a non-dominated archived multi-objective harmony search (NAMHS) algorithm to identify influencers in social networks. This algorithm can generate a set of Pareto optimal harmony vectors for addressing the multi-objective IM problems. Our proposed algorithm is compared with two other state-of-the-art algorithms, and the performance of our algorithm is higher. We also propose a dynamic non-dominated archived multi-objective harmony search (DNAMHS) algorithm for dynamic networks and demonstrate that it performs better than the current algorithms. Moreover, the time complexity of the proposed NAMHS and DNAMHS algorithms is comparable with that of existing algorithms. The deterministic linear threshold model is used for influence propagation in both static and dynamic networks.