[Objective] To achieve a trade-off between running efficiency and accuracy, this paper proposes a voting-based algorithm for identifying a set of influential nodes in social networks named KSEVoteRank. [Methods] Considering the nodal importance and neighborhood information, node’s voting ability is defined and voting allocation strategy is designed. Meanwhile, an attenuation factor is introduced to discount the voting ability of neighbors. Finally, the node with the highest voting score is iteratively selected as the seed node. [Results] The experimental results shows that the impact overlap of a set of influential nodes detected by KSEVoteRank algorithm in the large social network Ca-AstroPh data set is about 21% less than that of VoteRank algorithm. [Limitations] During the repeated voting process, the voting allocation strategy of neighbors is fixed, which might cause a slight deviation in the theoretical results. [Conclusions] KSEVoteRank algorithm based on voting mechanism dispersedly selects a set of influential nodes to achieve a widespread propagation of influence, which is applicable to large-scale social networks.