Community discovery is an important topic of network representation learning. Manifold learning has been widely applied to network representation learning. However, most manifold learning algorithms do not consider the asymmetry of edges which is not accord with the structure of social networks because the influence of nodes is not symmetrical. In this paper, a community discovery algorithm based on manifold graph embedding with structure information propagation mechanism is proposed. The proposed algorithm uses high order approximation matrix to obtain the local and global structure information of a graph, then low rank decomposition is introduced to obtain the node vectors and the context vectors. Finally, the node vectors can be adjusted by structure information. The proposed algorithm and comparison algorithms are conducted on the experimental data sets. The experimental results show that the proposed algorithm outperforms the comparison algorithms on the most experimental data sets. The experimental results prove that the proposed algorithm is an effective algorithm for community discovery.

社区发现是网络表示学习的重要主题。流形学习已广泛应用于网络表示学习。但是，大多数流形学习算法都没有考虑边缘的不对称性，这与社交网络的结构不符，因为节点的影响是不对称的。提出了一种基于流形图嵌入和结构信息传播机制的社区发现算法。该算法利用高阶近似矩阵获取图的局部和全局结构信息，然后引入低秩分解得到节点向量和上下文向量。最后，可以通过结构信息来调整节点向量。在实验数据集上进行了所提出的算法和比较算法。实验结果表明，该算法在大多数实验数据集上均优于比较算法。实验结果表明，该算法是一种有效的社区发现算法。