An Effective Simulated Annealing for Influence Maximization Problem of Online Social Networks

The influence maximization problem (IMP) is one of the most well-known problems in the research domain of online social networks (OSN) that has attracted the attention of many researchers from different disciplines in recent years. One of the reasons is that the speed of information propagation in the OSN can be increased if we can find out users that have maximum influence on other users. The traditional rule-based and heuristic algorithms may not be able to find useful information out of these data because the data are generally large and complex. Although metaheuristic algorithms can be used to solve the IMP, there is still plenty of room for improvement. That is why an effective and efficient algorithm is presented in this paper. The proposed algorithm, called simulated annealing with search partition (SASP), is based on a search space partitioning mechanism to enhance the search performance of simulated annealing for the IMP. The experimental results show that the proposed algorithm outperforms the other state-of-the-art influence maximization problem algorithms compared in this paper in terms of the quality of the end result and the number of objective function evaluations.