社区检测是复杂网络中的研究热点，理解和发现网络的社区结构对于探索网络的行为和功能具有重要意义。提 出了一种新颖的基于邻居相似性的图嵌入方法进行社区检测。基于节点的邻居相似性和接受度聚合邻居的属性信息表达，得 到网络中每个节点的向量表达后，直接进行K-均值聚类得到最终的社区划分结果。实验结果表明：提出的算法具有更好的社 团划分结果，其模块性和标准归一化指标都有明显的提升。

Community detection is a crucial research topic in the realm of complex networks. Understanding and identifying the community structure of a network is essential for uncovering its behavior and function. In this paper, we propose a novel graph em‐ bedding method based on neighbor similarity for community detection. By utilizing the acceptance of nodes and aggregating attri‐ bute information expressions of neighbors, we obtain the vector representation of each node in the network. The final community de‐ tection results are then obtained by directly applying K-means clustering. Our experimental results demonstrate that our proposed al‐ gorithm outperforms other methods, showing significant improvements in both modularity and standard normalization metrics.