论文标题
跨网络节点分类的对抗深网嵌入
Adversarial Deep Network Embedding for Cross-network Node Classification
论文作者
论文摘要
在本文中，研究了跨网络节点分类的任务，该任务利用源网络中的大量标记的节点来帮助对目标网络中的未标记节点进行分类。现有的域适应算法通常无法对网络结构信息进行建模，而当前的网络嵌入模型主要集中在单网络应用程序上。因此，它们俩都不能直接应用于解决跨网络节点分类问题。这促使我们提出了一个对抗性跨网络深网嵌入（ACDNE）模型，以将对抗域的适应性与深网嵌入，以学习网络不变的节点表示，也可以很好地保留网络结构信息。在ACDNE中，深网嵌入模块利用两个特征提取器来保留节点之间的归因亲和力和拓扑接近。此外，还合并了一个节点分类器以使节点表示标签 - 歧义。此外，采用了对抗域的适应技术来使节点表示网络不变。广泛的实验结果表明，所提出的ACDNE模型在跨网络节点分类中实现了最新性能。
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.
