Joint Domain Adaptive Graph Convolutional Network

Joint Domain Adaptive Graph Convolutional Network - IJCAI

We propose a joint adversarial domain adaptive graph convolutional network (JDA-GCN) that is uniquely augmented with structural graph alignment.

Joint Domain Adaptive Graph Convolutional Network - IJCAI

To address this issue, we propose a joint adversarial domain adaptive graph convolutional network (JDA-GCN) that is uniquely augmented with structural graph.

Joint Domain Adaptive Graph Convolutional Network | Request PDF

Request PDF | Joint Domain Adaptive Graph Convolutional Network | In the realm of cross-network tasks, graph domain adaptation is an ...

TO-UGDA: target-oriented unsupervised graph domain adaptation

Unsupervised domain adaptive graph convolutional networks. In ... Jgcl: Joint self-supervised and supervised graph contrastive learning.

SA-GDA: Spectral Augmentation for Graph Domain Adaptation - arXiv

... graph convolutional network to jointly exploits local and global consistency for feature aggregation. Last, we utilize a domain classifier ...

Multi-label image classification using adaptive graph convolutional ...

Multi-label image classification using adaptive graph convolutional networks: From a single domain to multiple domains ... Deep transfer learning with joint ...

Unsupervised Domain Adaptive Graph Convolutional Networks

To enable effec- tive graph representation learning, we first develop a dual graph convolutional network component, which jointly exploits local and global ...

Open-Set Graph Domain Adaptation via Separate Domain Alignment

We jointly consider two different domain alignment strategies for ... Unsupervised Domain Adaptive Graph Convolutional Net- works. In WWW, WWW '20 ...

Domain-adaptive Graph Attention-supervised Network for Cross ...

Abstract—Graph neural networks (GNNs) have shown great ability in modeling graphs, however, their performance would significantly degrade when there are ...

Graph Convolutional Adversarial Network for Unsupervised Domain ...

We propose an end-to-end Graph Convolutional Adversarial Network (GCAN) for unsupervised domain adaptation by jointly modeling data structure, domain label, ...

A Multi-Domain Adaptive Graph Convolutional Network for EEG ...

In this paper, we propose a Multi-Domain Adaptive Graph Convolutional Network (MD-AGCN), fusing the knowledge of both the frequency domain and the temporal ...

Domain-Adaptive Graph Attention-Supervised Network for Cross ...

Graph neural networks (GNNs) have shown great ability in modeling graphs ... jointly trains node embeddings and edge embeddings via the ...

Center-aligned domain adaptation network for image classification

A joint adversarial domain adaptive graph convolutional network (JDA-GCN) is proposed that is uniquely augmented with structural graph alignment, so as to ...

kaize0409/Awesome-Graph-OOD - GitHub

[ICJAI 2024] Joint domain adaptive graph convolutional network, [N/A]. HC-GST, Invariant Representation Learning, [CIKM 2024] HC-GST: Heterophily-aware ...

Two-Stream Adaptive Graph Convolutional Networks for Skeleton ...

These methods perform the graph convolution in the frequency domain with the help of ... rent joint and the 25th joint in the learned adaptive graph of our model.

Part 6: unsupervised domain adaptive graph convolutional networks

... available. Part 6: unsupervised domain adaptive graph convolutional networks. 63 views · 5 months ago ...more. Farshad Noravesh. 2.5K.

Coronary heart disease prediction method fusing domain-adaptive ...

Graph convolutional networks (GCNs) have achieved impressive results in many medical scenarios involving graph node classification tasks.

Unsupervised Domain Adaptive Graph Convolutional Networks

To enable effec- tive graph representation learning, we first develop a dual graph convolutional network component, which jointly exploits local and global ...

Unsupervised Domain Adaptive Graph Convolutional Networks

To enable effective graph representation learning, we first develop a dual graph convolutional network component, which jointly exploits local ...

GNN Domain Adaptation using Optimal Transport - OpenReview

We analyze the OOD generalization and consequent domain adaptation limits of Graph Convolution Networks. An optimal transport based DA method is proposed.