Identifying influential spreads in a network is of great significance for the analysis and control of the information dissemination process in complex networks. Based on the network embedding method, we propose an algorithm to identify the high influence nodes of the network. Firstly, the DeepWalk network embedding algorithm is used to map the high-dimensional complex network to a low-dimensional vector space to calculate the Euclidean distance between the local node pairs. Then, combined with the network topology information, a local centrality index of the network nodes is proposed to identify the high influence nodes. In eight real networks, the new algorithm is compared with five well-known identification methods. Numerical simulation results show that the new algorithm has a good performance in identifying influential spreaders.

识别网络中有影响的传播对于分析和控制复杂网络中的信息传播过程具有重要意义。基于网络嵌入方法，提出了一种识别网络中影响较大的节点的算法。首先，使用DeepWalk网络嵌入算法将高维复杂网络映射到低维向量空间，以计算局部节点对之间的欧几里得距离。然后，结合网络拓扑信息，提出网络节点的局部集中度指标，以识别高影响力节点。在八个真实网络中，将新算法与五种著名的识别方法进行了比较。数值仿真结果表明，该算法在识别有影响的吊具方面具有良好的性能。