Domain adaptation using graph-structured networks learns label-discriminative and network-invariant node embeddings by sharing graph parameters. Most existing works focus on domain adaptation of homogeneous networks. The few works that study heterogeneous cases only consider shared node types but ignore private node types in individual networks. However, for given source and target heterogeneous networks, they generally contain shared and private node types, where private types bring an extra challenge for graph domain adaptation. In this paper, we investigate Heterogeneous Information Networks (HINs) with both shared and private node types and propose a Generalized Domain Adaptive model across HINs (GDA-HIN) to handle the domain shift between them. GDA-HIN can not only align the distribution of identical-type nodes and edges in two HINs but also make full use of different-type nodes and edges to improve the performance of knowledge transfer. Extensive experiments on several datasets demonstrate that GDA-HIN can outperform state-of-the-art methods in various domain adaptation tasks across heterogeneous networks.
翻译:使用图形结构化网络进行域适应,通过共享图形参数来学习标签差异性和网络差异性节点嵌入。大多数现有工作都侧重于同质网络的域性适应。研究不同案例的少数工作只考虑共享节点类型,而忽略了单个网络中的私人节点类型。然而,对于特定源和目标差异性网络来说,它们通常包含共享和私人节点类型,私人类型对图形域适应带来额外的挑战。在本文中,我们调查具有共享和私有节点类型的异种信息网络(HINs),并提议一个跨 HINs(GDA-HIN)的通用适应模型,以处理它们之间的域转移。GDA-HIN不仅能够将两个 HIN 的相同类型节点和边缘的分布相匹配,而且还充分利用不同类型节点和边缘来改进知识转让的绩效。关于若干数据集的大规模实验表明,GDA-HIN 能够超越不同不同领域适应任务中的状态方法。