Cross-network node classification (CNNC), which aims to classify nodes in a
label-deficient target network by transferring the knowledge from a source
network with abundant labels, draws increasing attention recently. To address
CNNC, we propose a domain-adaptive message passing graph neural network
(DM-GNN), which integrates graph neural network (GNN) with conditional
adversarial domain adaptation. DM-GNN is capable of learning informative
representations for node classification that are also transferrable across
networks. Firstly, a GNN encoder is constructed by dual feature extractors to
separate ego-embedding learning from neighbor-embedding learning so as to
jointly capture commonality and discrimination between connected nodes.
Secondly, a label propagation node classifier is proposed to refine each node's
label prediction by combining its own prediction and its neighbors' prediction.
In addition, a label-aware propagation scheme is devised for the labeled source
network to promote intra-class propagation while avoiding inter-class
propagation, thus yielding label-discriminative source embeddings. Thirdly,
conditional adversarial domain adaptation is performed to take the
neighborhood-refined class-label information into account during adversarial
domain adaptation, so that the class-conditional distributions across networks
can be better matched. Comparisons with eleven state-of-the-art methods
demonstrate the effectiveness of the proposed DM-GNN.

通过跨网络节点分类（CNNC）实现源网络知识向标签不足的目标网络的转移，提出了一种领域自适应传递图神经网络（DM-GNN），它将图神经网络（GNN）与条件对抗性领域自适应相结合，能够学习可传递到不同网络的节点分类的信息表示。