A depth-based heuristic to solve the multi-objective influence spread problem using particle swarm optimization

The influence spread in a social network is an iterative process that can take several steps. It begins with an activation seed and finishes when the current activation cannot influence more actors. The multi-objective influence spread problem corresponds to finding the smallest number of actors capable of maximizing the influence spread within the network. This problem has been solved by metaheuristic optimization algorithms using swarm intelligence methods. This article proposes a heuristic to improve the existing solution: when two sets of actors can influence the same number of actors, the one whose spread requires the least number of steps is chosen. The proposed solution is tested on two different real networks. The results show that the heuristic allowed better results for both networks and decreased the average number of steps in the influence spread processes (in 15.5 and 0.07 average steps, respectively), thus improving execution times. Moreover, the heuristic allowed decreasing the number of steps in 83% (against 17% of increasing) and 13% (against 7% of increasing) of the particles, respectively.