Dec 21, 2023 · Our method encompasses a dual-pronged structure, consisting of a graph convolutional network branch and a graph kernel branch, which enables us ...

This section presents our unsupervised domain adaptive graph convolutional networks for cross-domain node classification. 4.1 Node Embedding Module. In order ...

We introduce a new approach for unsupervised domain adaptive graph classification, named CoCo, which con- tains a graph convolutional network branch and a hier-.

Dec 21, 2023 · We introduce the practical problem of unsupervised domain adaptive graph classification named DAGRL. DAGRL is proposed with two branches, i.e., ...

Apr 22, 2024 · Graph domain adaptation (GDA) aims to address the challenge of limited label data in the target graph domain. Existing methods such as ...

The results indicate the following observations: 1) Domain adaptation methods outperform graph kernel and GNN methods, suggesting that current graph ...

We propose a disentanglement-based unsupervised domain adaptation method for the graph-structured data, which applies variational graph auto-encoders to ...

This repository contains the paper list of Graph Domain Adaptation (GDA). The existing literature can be mainly categorized into three categories.

Jul 1, 2024 · A novel graph-based approach for multi-label image classification is proposed. It learns adaptively a graph describing label dependencies.

In this paper, we present a novel approach, unsupervised domain adaptive graph convolutional networks (UDA-GCN), for domain adaptation learning for graphs.