论文标题
GDA-HIN：跨异构信息网络的广义域自适应模型
GDA-HIN: A Generalized Domain Adaptive Model across Heterogeneous Information Networks
论文作者
论文摘要
使用图形结构化网络的域适应性通过共享图形参数来学习标签 - 歧义和网络不变的节点嵌入。大多数现有作品都集中在域均匀网络的适应上。研究异质案例的少数作品仅考虑共享节点类型，但忽略了单个网络中的私人节点类型。但是，对于给定的源和目标异质网络，它们通常包含共享和私有节点类型，其中私有类型为图形域改编带来了额外的挑战。在本文中，我们研究了具有共享和私有节点类型的异质信息网络（HINS），并提出了跨HINS（GDA-HIN）的广义域自适应模型，以处理它们之间的域移动。 GDA-HIN不仅可以使相同的型节点和边缘的分布在两个呼吸中对齐，而且还可以充分利用不同型节点和边缘来提高知识传递的性能。在几个数据集上进行的广泛实验表明，GDA-HIN可以在各种域名网络的各种域适应任务中胜过最先进的方法。
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.
