Multi|branch fusion graph neural network based on multi|head ...

Multi-branch fusion graph neural network based on multi-head ...

The multi-branch graph convolutional network is employed to dynamically learn temporal correlations and spatial topological structures. Utilizing the multi-head ...

Multi-branch fusion graph neural network based on multi-head ...

PDF | On Nov 1, 2024, Yang Li and others published Multi-branch fusion graph neural network based on multi-head attention for childhood ...

Graph features dynamic fusion learning driven by multi-head ...

... based on graph neural networks (GNN) have received some satisfactory achievements. But most of them are based on the analysis of the single sensor signals ...

An adaptive multi-graph neural network with multimodal feature ...

proposed a multimodal fusion strategy based on graph neural networks to explore potential relationships between samples. Current research ...

Multi-view fusion based Heterogeneous Graph Neural Network

A Multi-view fusion based Heterogeneous Graph Neural Network (MHGNN) is proposed, which is modeled from node view, network schema view, and semantics view.

Multi-type feature fusion based on graph neural network for drug ...

In this paper, we propose a multi-type feature fusion based on graph neural network model (MFFGNN) for DDI prediction, which can effectively fuse the ...

A graph convolutional neural network model based on fused multi ...

The graph convolution neural network (GCN)-based node classification model tackles the challenge of classifying nodes in graph data through learned feature ...

An adaptive multi-graph neural network with multimodal ... - PubMed

An adaptive multi-graph neural network with multimodal feature fusion learning for MDD detection · Authors · Affiliations.

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 ...

[2307.07093] MaxCorrMGNN: A Multi-Graph Neural Network ... - arXiv

We develop an innovative fusion approach called MaxCorr MGNN that models non-linear modality correlations within and across patients.

Spectral Graph Neural Network-Based Multi-Atlas Brain ... - PubMed

Spectral Graph Neural Network-Based Multi-Atlas Brain Network Fusion for Major Depressive Disorder Diagnosis. IEEE J Biomed Health Inform. 2024 May;28(5): ...

Learning a Graph Neural Network with Cross Modality Interaction for ...

... network (GNN)-based architecture between cross modality for fusion, called IGNet. Specifically, we first apply a multi-scale extractor to ...

Multi-graph attention fusion graph neural network for remaining ...

A multi-graph structure GNN prediction method with attention fusion (MGAFGNN) is proposed in this paper for GNN-based bearing RUL prediction.

Multi-level attention graph neural network based on co-expression ...

... multi-level graph feature fully fusion module to conduct predictions. For ... Veličković et al. (2017), we employ multi-head attention to stabilize the learning ...

TransG-net: transformer and graph neural network based multi ...

TransG-net: transformer and graph neural network based multi-modal data fusion network for molecular properties prediction. Published: 01 ...

Multi-scale Fusion Dynamic Graph Neural Network For Traffic Flow ...

Zulong Diao, Xin Wang, Dafang Zhang, Yingru Liu, Kun Xie, and Shaoyao He. 2019. Dynamic spatial-temporal graph convolutional neural networks for ...

Dual Fusion-Propagation Graph Neural Network for Multi-View ...

This work proposes an efficient model termed Dual Fusion-Propagation Graph Neural Network (DFP-GNN) and applies it to deep multi-view clustering tasks and ...

Multi-Relationship and Multi-Attribute Fusion Based on Graph ...

Multi-Relationship and Multi-Attribute Fusion Based on Graph Neural Network for API Recommendation. Jie Cao, Rong Hu. 2023 International ...

Deep multi-graph neural networks with attention fusion for ...

How to obtain preferable latent representations for both users and items is one of the key issues for GNN-based recommendation. This paper develops a novel deep ...

GRAPH NEURAL NETWORK BASED MULTI-FEATURE FUSION ...

Keywords: Graph Neural Network, Feature extraction, Multi-feature fusion, Dense Image Matching, Building Change Detection, Node aggregation.