In this paper, the task of cross-network node classification, which leverages the abundant labeled nodes from a source network to help classify unlabeled nodes in a target network, is studied. The existing domain adaptation algorithms generally fail to model the network structural information, and the current network embedding models mainly focus on single-network applications. Thus, both of them cannot be directly applied to solve the cross-network node classification problem. This motivates us to propose an adversarial cross-network deep network embedding (ACDNE) model to integrate adversarial domain adaptation with deep network embedding so as to learn network-invariant node representations that can also well preserve the network structural information. In ACDNE, the deep network embedding module utilizes two feature extractors to jointly preserve attributed affinity and topological proximities between nodes. In addition, a node classifier is incorporated to make node representations label-discriminative. Moreover, an adversarial domain adaptation technique is employed to make node representations network-invariant. Extensive experimental results demonstrate that the proposed ACDNE model achieves the state-of-the-art performance in cross-network node classification.

本文介绍一种基于对抗性领域适应的深度网络嵌入模型，旨在实现跨网络节点分类。该模型利用两个特征提取器来保留节点之间的属性相似性和拓扑关系，并采用节点分类器和对抗性领域适应技术来使节点表示网络不变和有较好的标签判别能力。实验结果表明，该模型在跨网络节点分类问题上达到了最先进的表现。

跨网络节点分类的对抗深度网络嵌入