Dual|channel deep graph convolutional neural networks

Dual-channel deep graph convolutional neural networks - Frontiers

A dual-channel deep graph convolutional neural network (D2GCN) is proposed, which can effectively avoid over-smoothing and improve model performance.

Dual-channel deep graph convolutional neural networks - PMC - NCBI

Abstract. The dual-channel graph convolutional neural networks based on hybrid features jointly model the different features of networks, so ...

Dual-channel deep graph convolutional neural networks - PubMed

The State Key Laboratory of Tibetan Intelligent Information Processing and Application, Xining, Qinghai, China. 3 Tibetan Information Processing ...

(PDF) Dual-channel deep graph convolutional neural networks

PDF | The dual-channel graph convolutional neural networks based on hybrid features jointly model the different features of networks, ...

DeepMCGCN: Multi-channel Deep Graph Neural Networks

This paper proposes a multi-channel deep graph convolutional neural network method called DeepMCGCN. It constructs multiple relational subgraphs and adopts ...

Multi-view dual-channel graph convolutional networks with multi ...

However, existing studies mainly focus on single task or single view and cannot obtain deeper relevant information for accomplishing tasks. In ...

Adaptive Multi-Channel Deep Graph Neural Networks - MDPI

The message passing mechanism in GNNs is inspired by the convolution operation of convolutional neural networks (CNNs) [19]. In CNNs, a deeper layer makes a ...

A novel dual-channel graph convolutional neural network for facial ...

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. (2019). Z. Shao et al. Deep adaptive attention for joint facial action unit ...

Multi-Channel Graph Neural Networks - IJCAI

It is comparable with the pooling layer in convolutional neu- ral networks (CNN) [Krizhevsky et al., 2012], which coarsen image resolution to extract the ...

A deep graph convolutional neural network architecture for ... - PLOS

The designed deep graph convolutional layer receives the original graph as input and outputs the feature matrix of the graph. Deep graph ...

Graph Convolutional Networks - Oxford Geometric Deep Learning

In this video, I go over Graph Convolutional Networks! Excellent blog post on GCNs (from one of the authors): ...

[PDF] DAGCN: Dual Attention Graph Convolutional Networks

CayleyNets: Graph Convolutional Neural Networks With Complex Rational ... A new spectral domain convolutional architecture for deep learning on graphs ...

Co-embedding of edges and nodes with deep graph convolutional ...

We aspire to develop a deep graph convolutional neural network learning framework capable of simultaneously acquiring edge embeddings and node embeddings.

Dual Graph Convolutional Networks for Graph-Based Semi ...

Accordingly, two convolutional neural networks are devised to embed the local-consistency-based and global-consistency-based knowledge, respectively. Given the ...

Dual graph convolutional neural network for predicting chemical ...

More recently, driven by the significant advances of deep neural networks, researchers are moving to automatic extraction of flexible and ...

Dual Attention Graph Convolutional Networks - DAGCN - arXiv

the graph. Deep learning-based approaches like graph neural network have also been applied diffusely for network representation. These ...

Learning Dual Convolutional Neural Networks for Low-Level Vision

Figure 6. Visual comparisons of deep learning-based methods for image deraining on real examples. The proposed method is able to remove rainy streaks from the ...

An attention enhanced dual graph neural network for mesh denoising

Graph convolution neural networks (GCNs) are a powerful tool for processing non-Euclidean structures. They define features on nodes and pass them through edges ...

A Dual-Branch Fusion of a Graph Convolutional Network and ... - MDPI

A dual-branch fusion of a GCN and convolutional neural network (DFGCN) is proposed for HSIC tasks. The GCN branch uses an adaptive multi-scale superpixel ...

An End-to-End Deep Learning Architecture for Graph Classification

For example, if we randomly shuffle the pixels of an image shown in Figure 1, then state-of-the-art convolutional neural networks (CNN) fail to recognize it as ...