为了利用跨域图来学习用于节点分类的分类器,文章提出了一种无监督的域自适应图卷积网络(Unsupervised Domain Adaptive Graph Convolutional Networks, UDA-GCN),以缩小分布差距并产生跨域共享的低维特征表示。如图2所示,UDA-GCN框架主要由以下三个组件构成: 节点表示学习。为更好地学习每个节点的表示,使用了一种对偶图卷...
论文标题:Unsupervised Domain Adaptive Graph Convolutional Networks论文作者:论文来源:2020 aRxiv论文地址:download 论文代码:download视屏讲解:click 1-摘要图卷积网络(GCNs)在许多与图相关的分析任务中都取得了令人印象深刻的成功。然而,大多数 GCN 只在一个域(图)中工作,无法将知识从其他域(图)转移,因为图表示...
3.1 DANE: Domain Adaptive Network Embedding (IJCAI '19) 3.2 GCAN: Graph Convolutional Adversarial Network for Unsupervised Domain Adaptation (CVPR '19) 3.3 Unsupervised Domain Adaptive Graph Convolutional Networks (WWW '20) 3.4 Adaptive Trajectory Prediction via Transferable GNN 3.5 Curriculum Graph Co-...
We also incorporate graph convolutional networks (GCNs) to extract graph-level embeddings, a feature that has been largely overlooked in previous WiFi indoor localization studies. To deal with the challenges of large amounts of unlabeled data and multiple data domains, we employ a semisupervised ...
All data generated or analyzed during this study are included in this manuscript. The source code of the model and the datasets can be accessed from “https://github.com/xueyutao/Domain-adaptive-Transfer-Learning-with-Graph-Convolutional-Networks”. ...
MRF_GCN: @ARTICLE{MRF_GCN, author={T. {Li} and Z. {Zhao} and C. {Sun} and R. {Yan} and X. {Chen}}, journal={IEEE Transactions on Industrial Electronics}, title={Multi-receptive Field Graph Convolutional Networks for Machine Fault Diagnosis}, year={2020}, volume={}, number={},...
Hence, it is important to design a network embedding algorithm that supports downstream model transferring on different networks, known as domain adaptation. In this paper, we propose a novel Domain Adaptive Network Embedding framework, which applies graph convolutional network to learn transferable ...
Chen X, Wan X (2022) A simple information-based approach to unsupervised domain-adaptive aspect-based sentiment analysis. arXiv preprint arXiv:2201.12549 Zhou J, Huang JX, Hu QV, He L (2020) Sk-gcn: modeling syntax and knowledge via graph convolutional network for aspect-level sentiment class...
images. Methods This study presents a semi‐supervised graph‐convolutional‐network‐based domain adaptation framework, namely Semi‐GCNs‐DA. Based on the ResNet backbone, it is extended in three aspects for domain adaptation, that is, graph convolutional networks (GCNs) for the connection ...
本文提出了DANE(Domain Adaptive Network Embedding),一种无监督网络嵌入框架:通过GCN(graph convolutional network)和对抗学习(adversarial learning)来解决嵌入空间漂移和分布偏移。 为使GCN保持跨网络节点对(cross-network node pairs)的结构相似性,我们采用了共享权重体系结构,这意味着GCN通过共享的可学习参数向量映射源...