Network embedding is to learn low-dimensional representations of nodes while preserving necessary information for network analysis tasks. Though representations preserving both structure and attribute features have achieved in many real-world applications, learning these representations for networks with attribute information is difficult due to the heterogeneity between structure and attribute information. Many existing methods have been proposed to preserve explicit proximities between nodes, with optimization limited to node pairs with large structure and attribute proximities, which may lead to overfitting. To address the above problems, we adopt an attribute augmented network to represent attribute and structure information in a unified framework. Specifically, we study the problem of attribute augmented network embedding that exploits the strength of generative adversarial nets (ANGANs) in capturing the latent distribution of data to learn robust and informative representations of nodes. The ANGAN method obtains the low-dimensional representations of nodes through adversarial learning between the generative and discriminative models. The generative model approximates the underlying connectivity and attributes distributions of nodes by using the distributions generated from the learned representations. It is implemented by utilizing the properties of the attribute augmented network to improve the traditional Skip-gram model. The discriminative model is designed as a binary classifier to distinguish the truly connected node pairs from the generated ones. The pre-training algorithm and the teacher forcing approach are adopted to improve training efficiency and stability. Empirical results show that ANGAN generally outperforms state-of-the-art methods in various real-world applications, which demonstrates the effectiveness and generality of our method.

中文翻译：

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基于生成对抗网络的属性增强网络嵌入

网络嵌入是学习节点的低维表示，同时保留网络分析任务所需的信息。尽管在许多实际应用中已经实现了保留结构和属性特征的表示，但由于结构和属性信息之间的异质性，学习具有属性信息的网络的这些表示是困难的。已经提出了许多现有方法来保留节点之间的显式邻近度，优化仅限于具有较大结构和属性邻近度的节点对，这可能导致过度拟合。为了解决上述问题，我们采用属性增强网络在统一的框架中表示属性和结构信息。具体来说，我们研究了属性增强网络嵌入问题，该问题利用生成对抗网络（ANGAN）的优势来捕获数据的潜在分布，以学习节点的稳健且信息丰富的表示。ANGAN方法通过生成模型和判别模型之间的对抗性学习获得节点的低维表示。生成模型通过使用从学习的表示生成的分布来近似节点的底层连接性和属性分布。它是利用属性增强网络的特性来改进传统的Skip-gram模型来实现的。判别模型被设计为二元分类器，以区分真正连接的节点对和生成的节点对。采用预训练算法和教师强制方式，提高训练效率和稳定性。实证结果表明，ANGAN 在各种实际应用中通常优于最先进的方法，这证明了我们方法的有效性和通用性。