Network embedding is a highly effective method to learn low-dimensional node vector representations with original network structures being well preserved. However, existing network embedding algorithms are mostly developed for a single network, which fails to learn generalized feature representations across different networks. In this article, we study a cross-network node classification problem, which aims at leveraging the abundant labeled information from a source network to help classify the unlabeled nodes in a target network. To succeed in such a task, transferable features should be learned for nodes across different networks. To this end, a novel cross-network deep network embedding (CDNE) model is proposed to incorporate domain adaptation into deep network embedding in order to learn label-discriminative and network-invariant node vector representations. On the one hand, CDNE leverages network structures to capture the proximities between nodes within a network, by mapping more strongly connected nodes to have more similar latent vector representations. On the other hand, node attributes and labels are leveraged to capture the proximities between nodes across different networks by making the same labeled nodes across networks have aligned latent vector representations. Extensive experiments have been conducted, demonstrating that the proposed CDNE model significantly outperforms the state-of-the-art network embedding algorithms in cross-network node classification.

中文翻译：

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Network Together：通过跨网络深度网络嵌入进行节点分类


网络嵌入是一种学习低维节点向量表示的高效方法，并且可以很好地保留原始网络结构。然而，现有的网络嵌入算法大多是针对单个网络开发的，无法学习跨不同网络的广义特征表示。在本文中，我们研究了跨网络节点分类问题，旨在利用源网络中丰富的标记信息来帮助对目标网络中的未标记节点进行分类。为了成功完成这样的任务，应该为不同网络上的节点学习可转移的特征。为此，提出了一种新颖的跨网络深度网络嵌入（CDNE）模型，将域适应纳入深度网络嵌入中，以学习标签判别性和网络不变的节点向量表示。一方面，CDNE 利用网络结构来捕获网络内节点之间的邻近性，通过映射更紧密连接的节点来具有更相似的潜在向量表示。另一方面，通过使跨网络的相同标记节点具有对齐的潜在向量表示，利用节点属性和标签来捕获跨不同网络的节点之间的邻近度。广泛的实验表明，所提出的 CDNE 模型在跨网络节点分类方面显着优于最先进的网络嵌入算法。