Graph convolutional networks with the self|attention mechanism for ...

Multistep Flow Prediction on Car-Sharing Systems: A Multi-Graph ...

In this paper, we propose an attention multi-graph convolutional sequence- to-sequence model (AMGC-Seq2Seq), which is a novel deep learning model for multistep ...

Multi-View GCNs with Attention Mechanism (MAGCN) - GitHub Pages

Recent advances in graph convolutional networks (GCNs), mainly focusing on how to exploit the information from different hops of neighbors in an efficient way, ...

Graph Attention Networks (GAT) in 5 minutes - YouTube

Join my FREE course Basics of Graph Neural Networks (https://www.graphneuralnets.com/p/basics-of-gnns/?src=yt) GAT paper: ...

GCMM: graph convolution network based on multimodal attention ...

The main focus of in silico drug repurposing, which is a promising area for using artificial intelligence in drug discovery, ...

On the Global Self-attention Mechanism for Graph Convolutional ...

... Convolutional Neural Networks (CNNs). However, it is not clear ... On the Global Self-attention Mechanism for Graph Convolutional Networks.

Graph Convolutional Networks with Motif-based Attention

deep learning models for graphs. The work of Velickovic et al. [42] used a node self-attention mechanism to allow each node to focus on features in its ...

Dynamic graph convolutional network for assembly behavior ...

This paper proposes a graph convolutional network model for assembly behavior recognition based on attention mechanism and multi-scale feature fusion.

Adaptive Propagation Graph Convolutional Networks Based ... - MDPI

In this paper, a new adaptive propagation graph convolutional network model based on the attention mechanism (APAT-GCN) is proposed.

Graph Convolutional Recommendation System Based on Bilateral ...

Consequently, a Bilateral Attention Mechanism Graph Convolutional Network Recommender Model (BAM-GCN) is proposed. The innovations of this paper ...

Multi-View Feature Enhancement Based on Self-Attention ... - PubMed

Multi-View Feature Enhancement Based on Self-Attention Mechanism Graph Convolutional Network for Autism Spectrum Disorder Diagnosis. Front ...

Graph Convolutional Networks with Motif-based Attention

Sami Abu-El-Haija, Amol Kapoor, Bryan Perozzi, and Joonseok Lee. 2018. N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification. In arXiv: ...

[PDF] Global Self-Attention as a Replacement for Graph Convolution

The findings indicate that global self-attention based aggregation can serve as a flexible, adaptive and effective replacement of graph convolution for general ...

Graph Convolutions Enrich the Self-Attention in Transformers!

This has led to the development of GNNs specifically for directed graphs, like DiGCN [10], MagNet, and Signed Graph Neural Networks [11], which use a ...

On the Global Self-attention Mechanism for Graph Convolutional ...

PDF | Applying Global Self-attention (GSA) mechanism over features has achieved remarkable success on Convolutional Neural Networks (CNNs).

A Self-Attention Augmented Graph Convolutional - ProQuest

In this paper, we propose a new method for detecting abnormal human behavior based on skeleton features using self-attention augment graph convolution.

Novel GCN Model Using Dense Connection and Attention ...

A novel Graph Convolutional Neural Network (GCN) with dense connections and an attention mechanism for text classification is proposed to address these ...

Adaptive Propagation Graph Convolutional Networks Based on ...

In this paper, a new adaptive propagation graph convolutional network model based on the attention mechanism (APAT-GCN) is proposed, which enables GNNs to ...

KAGN:knowledge-powered attention and graph convolutional ...

... graphs, attention mechanism, graph neural network. ... Combining contextual information by self-attention mechanism in convolutional neural ...

sunxiaobei/awesome-attention-based-gnns - GitHub

... graph convolutional networks via attention mechanism [paper]. Hierarchical ... [ICML2021] [] Lipschitz Normalization for Self-Attention Layers with Application to ...

Attention Based Spatial-Temporal Graph Convolutional Networks for ...

For classifying nodes of a graph, Velickovic et al. (2018) lever- aged self-attentional layers to process graph-structured data by neural networks and achieved ...