LIDDE: A differential evolution algorithm based on local-influence-descending search strategy for influence maximization in social networks

Abstract Influence maximization aims to select k seed nodes from social networks so that the expected number of nodes activated by the seed nodes can be maximized. With the development and popularity of Internet technology, the influence maximization has become a vital problem, especially for viral marketing. However, most existing algorithms utilize the greedy strategy to select seed nodes, which usually leads themselves into local optimal solution. Most other algorithms that not based on the greedy strategy usually have low efficiency. Therefore, a Local-Influence-Descending search strategy is proposed, which can obtain a node set in which each node has relatively large influence. Afterwards, based on this strategy, a new approach for influence maximization is proposed to solve these problems, called Local-Influence-Descending Differential Evolution (LIDDE). It can improve the accuracy as well as the computation efficiency of influence maximization algorithms based on swarm intelligence. Experimental results on six real-world social networks demonstrate that the proposed algorithm outperforms all comparison algorithms in terms of accuracy and all algorithms based on swarm intelligence in terms of efficiency.