Network embedding aims to map a complex network into a low-dimensional vector space while maximally preserving the properties of the original network. An attributed network is a typical real-world network that models the relationships and attributes of real-world entities. Its analysis is of great significance in many applications. However, most such networks are incomplete with partially-known attributes, links and labels. Traditional network embedding methods are designed for a complete network and cannot be applied to a network with incomplete information. Thus, this work proposes an inductive embedding model to learn the robust representations for a partially-unseen attributed network. It is designed based on a multi-core convolutional neural network and a semi-supervised learning mechanism, which can preserve the properties of such a network and generate the effective representations for unseen nodes in a model training process. We evaluate its performance on the task of inductive node classification and community detection via three real-world attributed networks. Experimental results show that it significantly outperforms the state-of-the-art.

中文翻译：

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通过CNN进行的部分未知属性网络的归纳表示学习

网络嵌入旨在将复杂的网络映射到低维向量空间，同时最大程度地保留原始网络的属性。属性网络是典型的现实世界网络，可对现实世界实体的关系和属性进行建模。它的分析在许多应用中具有重要意义。但是，大多数此类网络都不完整，缺少部分已知的属性，链接和标签。传统的网络嵌入方法是为完整的网络设计的，不能应用于信息不完整的网络。因此，这项工作提出了一种归纳嵌入模型，以学习部分未知属性网络的鲁棒表示。它是基于多核卷积神经网络和半监督学习机制而设计的，它可以保留此类网络的属性，并在模型训练过程中为看不见的节点生成有效的表示形式。我们通过三个真实的属性网络评估其在归纳节点分类和社区检测任务上的性能。实验结果表明，它明显优于最新技术。