Network embedding which aims to embed a given network into a low-dimensional vector space has been proved effective in various network analysis and mining tasks such as node classification, link prediction and network visualization. The emerging network embedding methods have shifted of emphasis in utilizing mature deep learning models. The neural-network based network embedding has become a mainstream solution because of its high efficiency and capability of preserving the nonlinear characteristics of the network. In this paper, we propose Adversarial Network Embedding using Structural Similarity (ANESS), a novel, versatile, low-complexity GAN-based network embedding model which utilizes the inherent vertex-to-vertex structural similarity attribute of the network. ANESS learns robustness and effective vertex embeddings via a adversarial training procedure. Specifically, our method aims to exploit the strengths of generative adversarial networks in generating high-quality samples and utilize the structural similarity identity of vertexes to learn the latent representations of a network. Meanwhile, ANESS can dynamically update the strategy of generating samples during each training iteration. The extensive experiments have been conducted on the several benchmark network datasets, and empirical results demonstrate that ANESS significantly outperforms other state-of-the-art network embedding methods.

旨在将给定网络嵌入到低维向量空间中的网络嵌入已被证明在各种网络分析和挖掘任务（例如节点分类，链接预测和网络可视化）中是有效的。新兴的网络嵌入方法已将重点转移到利用成熟的深度学习模型。基于神经网络的网络嵌入由于其高效和保持网络非线性特性的能力而成为主流解决方案。在本文中，我们提出使用结构相似性（ANESS）进行对抗性网络嵌入，这是一种新颖，通用，低复杂度的基于GAN的网络嵌入模型，该模型利用了网络固有的顶点到顶点结构相似性属性。ANESS通过对抗训练程序学习鲁棒性和有效的顶点嵌入。具体来说，我们的方法旨在利用生成对抗性网络在生成高质量样本中的优势，并利用顶点的结构相似性来学习网络的潜在表示形式。同时，ANSES可以在每次训练迭代期间动态更新生成样本的策略。对几个基准网络数据集进行了广泛的实验，经验结果表明，ANSES明显优于其他最新的网络嵌入方法。我们的方法旨在利用生成对抗性网络在生成高质量样本中的优势，并利用顶点的结构相似性来学习网络的潜在表示形式。同时，ANSES可以在每次训练迭代期间动态更新生成样本的策略。对几个基准网络数据集进行了广泛的实验，经验结果表明，ANSES明显优于其他最新的网络嵌入方法。我们的方法旨在利用生成对抗性网络在生成高质量样本中的优势，并利用顶点的结构相似性来学习网络的潜在表示形式。同时，ANSES可以在每次训练迭代期间动态更新生成样本的策略。对几个基准网络数据集进行了广泛的实验，经验结果表明，ANSES明显优于其他最新的网络嵌入方法。