Network embedding aims to represent vertices in the network with low-dimensional dense real number vectors, so that the attained vertices can acquire the ability of representation and inference in vector space. With the expansion of the scale of complex networks, how to make the high-dimensional network represented in low-dimensional vector space through network becomes an important issue. The typical algorithms of current autoencoder-based network embedding methods include DNGR and SDNE. DNGR method trains the Positive Pointwise Mutual Information (PPMI) matrix with the Stacked Denosing Autoencoder (SDAE), which is lacking in depth thereby attaining less satisfactory representation of network. Besides, SDNE used a semi-supervised autoencoder for embedding the adjacency matrix, whose sparsity may generate more cost in the learning process. In order to solve these problems, we propose a novel Autoencoder-based Network Embedding Algorithm (AENEA). AENEA is mainly divided into three steps. First, the random surfing model is used to process the original network to obtain the Probabilistic Co-occurrence (PCO) matrix between the nodes. Secondly, the Probabilistic Co-occurrence (PCO) matrix is processed to generate the corresponding Positive Pointwise Mutual Information (PPMI) matrix. Finally, the PPMI matrix is used to learn the representation of vertices in the network by using a semi-supervised autoencoder. We implemented a series of experiments to test the performance of AENEA, DNGR, SDNE and so on, on the standardized datasets 20-NewsGroup and Wine. The experimental results show that the performance of AENEA is obviously superior to the existing algorithms in clustering, classification and visualization tasks.

网络嵌入的目的是用低维的密集实数向量表示网络中的顶点，以便获得的顶点能够获得向量空间中的表示和推断能力。随着复杂网络规模的扩大，如何通过网络使低维向量空间表示的高维网络成为一个重要的问题。当前基于自动编码器的网络嵌入方法的典型算法包括DNGR和SDNE。DNGR方法使用深度不足的堆叠式Denosing自动编码器（SDAE）训练正点向互信息（PPMI）矩阵，因此无法获得令人满意的网络表示。此外，SDNE使用半监督自动编码器嵌入邻接矩阵，其稀疏性可能会在学习过程中产生更多成本。甲UTO Ë ncoder基于- Ñ etwork ê mbedding甲lgorithm（AENEA）。AENEA主要分为三个步骤。首先，使用随机冲浪模型处理原始网络，以获得节点之间的概率共现（PCO）矩阵。其次，处理概率共现（PCO）矩阵以生成相应的正点向互信息（PPMI）矩阵。最后，使用半监督自动编码器将PPMI矩阵用于学习网络中顶点的表示。我们在标准化数据集20-NewsGroup和Wine上实施了一系列实验，以测试AENEA，DNGR，SDNE等的性能。实验结果表明，在聚类，分类和可视化任务方面，AENEA的性能明显优于现有算法。