Search Results - graph convolutional autoencoder*

Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning by Park, Jiwoong, Lee, Minsik, Chang, Hyung Jin, Lee, Kyuewang, Choi, Jin Young

“…We propose a symmetric graph convolutional autoencoder which produces a low-dimensional latent representation from a graph…”
Get full text

Predicting transonic flowfields in non–homogeneous unstructured grids using autoencoder graph convolutional networks by Immordino, Gabriele, Vaiuso, Andrea, Da Ronch, Andrea, Righi, Marcello

“… Our approach leverages geometric deep learning, specifically through the use of an autoencoder architecture built on graph convolutional networks…”
Get full text

Semi-supervised overlapping community detection in attributed graph with graph convolutional autoencoder by He, Chaobo, Zheng, Yulong, Cheng, Junwei, Tang, Yong, Chen, Guohua, Liu, Hai

“… •An end-to-end method SSGCAE for overlapping community detection is proposed.•SSGCAE is based on graph convolutional autoencoder driven by community detection…”
Get full text

An Elliptic Kernel Unsupervised Autoencoder-Graph Convolutional Network Ensemble Model for Hyperspectral Unmixing by Alfaro-Mejia, Estefania, Delgado, Carlos J., Manian, Vidya

“… This article introduces the autoencoder graph ensemble model (AEGEM), a novel ensemble-based framework designed to enhance performance in both endmember extraction and abundance estimation…”
Get full text

Early Parkinson's Disease Prediction Using rS-fMRI Functional Connectivity and Autoencoder Graph Convolutional Network by Limas, Lesbia Lopez, Manian, Vidya

“… We propose a deep learning framework that combines resting-state functional MRI (rs-fMRI) data and a Graph Convolutional Network (GCN…”
Get full text

GCMCDTI: Graph convolutional autoencoder framework for predicting drug-target interactions based on matrix completion by Li, Jing, Zhang, Chen, Li, Zhengwei, Nie, Ru, Han, Pengyong, Yang, Wenjia, Liao, Hongmei

“… In this paper, we propose a novel model, named GCMCDTI, for DTIs prediction which adopts a graph convolutional network based on matrix completion…”
Get more information

Graph convolutional autoencoders with co-learning of graph structure and node attributes by Wang, Jie, Liang, Jiye, Yao, Kaixuan, Liang, Jianqing, Wang, Dianhui

“… Second, for existing graph autoencoders models, the encoder and decoder are mainly composed of an initial graph convolutional network (GCN…”
Get full text

A graph convolutional autoencoder approach to model order reduction for parametrized PDEs by Pichi, Federico, Moya, Beatriz, Hesthaven, Jan S.

“…The present work proposes a framework for nonlinear model order reduction based on a Graph Convolutional Autoencoder (GCA-ROM…”
Get full text

Convolutional Graph Autoencoder: A Generative Deep Neural Network for Probabilistic Spatio-Temporal Solar Irradiance Forecasting by Khodayar, Mahdi, Mohammadi, Saeed, Khodayar, Mohammad E., Wang, Jianhui, Liu, Guangyi

“… This probabilistic data generation model, i.e., convolutional graph autoencoder (CGAE), is devised based on the localized first-order approximation…”
Get full text

Graph convolutional autoencoder model for the shape coding and cognition of buildings in maps by Yan, Xiongfeng, Ai, Tinghua, Yang, Min, Tong, Xiaohua

“… A graph convolutional autoencoder (GCAE) model comprising graph convolution and autoencoder architecture is proposed to analyze…”
Get full text

Deformable Shape Completion with Graph Convolutional Autoencoders by Litany, Or, Bronstein, Alex, Bronstein, Michael, Makadia, Ameesh

“… The core of our method is a variational autoencoder with graph convolutional operations that learns a latent space for complete realistic shapes…”
Get full text

GCAEMDA: Predicting miRNA-disease associations via graph convolutional autoencoder by Li, Lei, Wang, Yu-Tian, Ji, Cun-Mei, Zheng, Chun-Hou, Ni, Jian-Cheng, Su, Yan-Sen

“…, diagnosis and treatment of extraordinary diseases. In this study, we presented a novel model named Graph Convolutional Autoencoder for miRNA-Disease Association Prediction (GCAEMDA…”
Get full text

Graph Convolutional Autoencoder and Generative Adversarial Network-Based Method for Predicting Drug-Target Interactions by Sun, Chang, Xuan, Ping, Zhang, Tiangang, Ye, Yilin

“… We proposed a graph convolutional autoencoder and generative adversarial network (GAN)-based method, GANDTI, to predict DTIs…”
Get full text

Drug-target interaction prediction based on graph convolutional autoencoder with dynamic weighting residual GCN by Zeng, Ming, Wang, Min, Xie, Fuqiang, Ji, Zhiwei

“… of network’s representation capabilities. Results In this paper, we propose a graph convolutional autoencoder model, named DDGAE, for DTIs prediction…”
Get full text

Graph Convolutional Autoencoder and Fully-Connected Autoencoder with Attention Mechanism Based Method for Predicting Drug-Disease Associations by Xuan, Ping, Gao, Ling, Sheng, Nan, Zhang, Tiangang, Nakaguchi, Toshiya

“…Predicting novel uses for approved drugs helps in reducing the costs of drug development and facilitates the development process. Most of previous methods…”
Get full text

Drug-target interaction prediction based on spatial consistency constraint and graph convolutional autoencoder by Chen, Peng, Zheng, Haoran

“… It may limit the performance of the DTI prediction methods. Results Here, we propose a novel graph convolutional autoencoder-based model, named SDGAE, to predict DTIs…”
Get full text

Federated learning enabled graph convolutional autoencoder and factorization machine for potential friendship prediction in social networks by Hu, He-xuan, Cao, Chengcheng, Hu, Qiang, Zhang, Ye

“… Therefore, we design a potential friendship prediction model based on graph convolutional autoencoder and factorization machine (GCAFM…”
Get full text

GVDTI: graph convolutional and variational autoencoders with attribute-level attention for drug–protein interaction prediction by Xuan, Ping, Fan, Mengsi, Cui, Hui, Zhang, Tiangang, Nakaguchi, Toshiya

“… First, a framework based on graph convolutional autoencoder is constructed to learn attention-enhanced topological embedding that integrates the topology structure…”
Get full text

MSGCA: Drug-Disease Associations Prediction Based on Multi-Similarities Graph Convolutional Autoencoder by Wang, Ying, Gao, Ying-Lian, Wang, Juan, Li, Feng, Liu, Jin-Xing

“… Hence, a prediction method based on multi-similarities graph convolutional autoencoder (MSGCA…”
Get full text

Seismic damage identification by graph convolutional autoencoder using adjacency matrix based on structural modes by Kim, Minkyu, Song, Junho

“…‐time damage identification by a graph convolutional autoencoder (GCAE) based on seismic responses of the structural system…”
Get full text