Výsledky vyhľadávania - Graph Convolutional Autoencoder

Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning Autor 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…”
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Predicting transonic flowfields in non–homogeneous unstructured grids using autoencoder graph convolutional networks Autor 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…”
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Semi-supervised overlapping community detection in attributed graph with graph convolutional autoencoder Autor 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…”
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An Elliptic Kernel Unsupervised Autoencoder-Graph Convolutional Network Ensemble Model for Hyperspectral Unmixing Autor 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…”
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Early Parkinson's Disease Prediction Using rS-fMRI Functional Connectivity and Autoencoder Graph Convolutional Network Autor 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…”
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GCMCDTI: Graph convolutional autoencoder framework for predicting drug-target interactions based on matrix completion Autor 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…”
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Graph convolutional autoencoders with co-learning of graph structure and node attributes Autor 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…”
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A graph convolutional autoencoder approach to model order reduction for parametrized PDEs Autor 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…”
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Convolutional Graph Autoencoder: A Generative Deep Neural Network for Probabilistic Spatio-Temporal Solar Irradiance Forecasting Autor 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…”
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Graph convolutional autoencoder model for the shape coding and cognition of buildings in maps Autor Yan, Xiongfeng, Ai, Tinghua, Yang, Min, Tong, Xiaohua

“… A graph convolutional autoencoder (GCAE) model comprising graph convolution and autoencoder architecture is proposed to analyze…”
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Deformable Shape Completion with Graph Convolutional Autoencoders Autor 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…”
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GCAEMDA: Predicting miRNA-disease associations via graph convolutional autoencoder Autor 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…”
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Graph Convolutional Autoencoder and Fully-Connected Autoencoder with Attention Mechanism Based Method for Predicting Drug-Disease Associations Autor 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…”
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Graph Convolutional Autoencoder and Generative Adversarial Network-Based Method for Predicting Drug-Target Interactions Autor 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…”
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Drug-target interaction prediction based on graph convolutional autoencoder with dynamic weighting residual GCN Autor 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…”
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Drug-target interaction prediction based on spatial consistency constraint and graph convolutional autoencoder Autor 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…”
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Federated learning enabled graph convolutional autoencoder and factorization machine for potential friendship prediction in social networks Autor 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…”
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MSGCA: Drug-Disease Associations Prediction Based on Multi-Similarities Graph Convolutional Autoencoder Autor Wang, Ying, Gao, Ying-Lian, Wang, Juan, Li, Feng, Liu, Jin-Xing

“… Hence, a prediction method based on multi-similarities graph convolutional autoencoder (MSGCA…”
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Seismic damage identification by graph convolutional autoencoder using adjacency matrix based on structural modes Autor Kim, Minkyu, Song, Junho

“…‐time damage identification by a graph convolutional autoencoder (GCAE) based on seismic responses of the structural system…”
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GVDTI: graph convolutional and variational autoencoders with attribute-level attention for drug–protein interaction prediction Autor 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…”
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