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.

影响力最大化旨在选择k社交网络中的种子节点，以便可以最大化由种子节点激活的预期节点数。随着Internet技术的发展和普及，影响力最大化已成为至关重要的问题，尤其是对于病毒性营销而言。然而，大多数现有算法利用贪婪策略来选择种子节点，这通常会使自己陷入局部最优解。大多数不基于贪婪策略的其他算法通常效率较低。因此，提出了一种局部影响递减的搜索策略，可以获取每个节点影响较大的节点集。然后，基于该策略，提出了一种解决影响最大的新方法，称为局部影响递减差分进化算法（LIDDE）。它可以提高基于群体智能的影响最大化算法的精度和计算效率。在六个现实世界社交网络上的实验结果表明，该算法在准确性方面优于所有比较算法，在效率方面优于所有基于群体智能的算法。