Domain adversarial graph neural network with cross|city ...

Domain adversarial graph neural network with cross-city graph ...

In this paper, we propose DAGN, a domain adversarial graph neural network that mines inter-city spatial–temporal correlations and alleviates domain ...

Domain adversarial graph neural network with cross-city graph ...

AbstractDeep learning models have emerged as a promising way for traffic prediction. However, the requirement for large amounts of training ...

Domain-Adversarial Graph Neural Networks for Text Classification

Text classification, in cross-domain setting, is a challenging task. On the one hand, data from other domains are often useful to improve the learning on ...

Domain adversarial graph neural network with cross-city ... - CoLab

Specifically, DAGN comprises three key modules: (1) A cross-city graph structure learning module is developed to capture node-pair adjacent ...

Domain adversarial graph neural network with cross-city ... - OUCI

Domain adversarial graph neural network with cross-city graph structure learning for traffic prediction ; Видання: Knowledge-Based Systems, 2023, с. 110885.

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 ...

Federated Graph Neural Network for Cross-graph Node Classification

... cross-graph nodes are learned. We add PATE mechanism into the domain adversarial neural network (DANN) to construct a cross-network node classification ...

Domain-Adversarial Graph Neural Networks for Text Classification

Abstract—Text classification, in cross-domain setting, is a challenging task. On the one hand, data from other domains.

Cross-Mode Knowledge Adaptation for Bike Sharing Demand ...

Cross-Mode Knowledge Adaptation for Bike Sharing Demand Prediction Using Domain-Adversarial Graph Neural Networks. For bike sharing systems ...

Domain-Adversarial Graph Neural Networks for Text Classification

PDF | Text classification, in cross-domain setting, is a challenging task. On the one hand, data from other domains are often useful to improve the.

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

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

Cross-Mode Knowledge Adaptation for Bike Sharing Demand ...

This study proposes a domain-adversarial multi-relational graph neural network (DA-MRGNN) for bike sharing demand prediction with multimodal historical data as ...

[PDF] Graph Transfer Learning via Adversarial Domain Adaptation ...

Domain-adaptive Graph Attention-supervised Network for Cross-network Edge Classification · Computer Science. IEEE Transactions on Neural Networks and Learning…

Adversarial Training for Graph Neural Networks: Pitfalls, Solutions...

Despite its success in the image domain, adversarial training did not (yet) stand out as an effective defense for Graph Neural Networks (GNNs) against graph ...

Domain-adaptive Message Passing Graph Neural Network - arXiv

Abstract:Cross-network node classification (CNNC), which ... graph neural network (GNN) with conditional adversarial domain adaptation.

GCAN: Graph Convolutional Adversarial Network for Unsupervised ...

lated to domain adaptation and graph neural networks. Domain ... Learning to discover cross-domain relations with generative adversarial networks.

Domain Adversarial Neural Network Research Articles - R Discovery

This paper proposes DACon, a Domain-Adversarial neural network-based cross ... Subsequently, the Graph Neural Networks (GNN) and long short-term memory ...

kaize0409/Awesome-Graph-OOD - GitHub

Invariant Representation Learning, [ICDM 2019] Domain-Adversarial Graph Neural Networks for Text Classification, [N/A]. DANE, Invariant Representation Learning ...

Unsupervised Domain Adaptive Graph Convolutional Networks

For cross- domain learning, many methods use an adversarial objective to reduce domain discrepancy [12, 20]. Among which, the domain adversarial neural network ...

Improving fake news detection with domain-adversarial and graph ...

Request PDF | Improving fake news detection with domain-adversarial and graph-attention neural network | With the widespread use of online social media, ...