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…”
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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…”
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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…”
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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…”
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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…”
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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…”
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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…”
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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…”
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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…”
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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…”
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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…”
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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…”
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Dynamic graph convolutional autoencoder with node-attribute-wise attention for kidney and tumor segmentation from CT volumes by Xuan, Ping, Cui, Hui, Zhang, Hongda, Zhang, Tiangang, Wang, Linlin, Nakaguchi, Toshiya, Duh, Henry B.L.

“…Extraction and integration of semantic connections, spatial relations and dependencies are critical in volumetric image segmentation. This is a challenging…”
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Human-related anomalous event detection via spatial-temporal graph convolutional autoencoder with embedded long short-term memory network by Li, Nanjun, Chang, Faliang, Liu, Chunsheng

“…Automatic detection of human-related anomalous events in surveillance videos is challenging, owing to unclear definition of anomalies and insufficiency of…”
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Spatio-temporal graph convolutional autoencoder for transonic wing pressure distribution forecasting by Immordino, Gabriele, Vaiuso, Andrea, Da Ronch, Andrea, Righi, Marcello

“…This study presents a framework for predicting unsteady transonic wing pressure distributions due to pitch and plunge movement, integrating an autoencoder…”
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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

“…Identification of potential drug-target interactions (DTIs) plays a pivotal role in the development of drug and target discovery in the public healthcare…”
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Deep Graph Convolutional Autoencoder With Conditional Normalizing Flow for Power Distribution Systems Fault Classification and Location by Saffari, Mohsen, Khodayar, Mahdi, Khodayar, Mohammad E., Fazlhashemi, Seyed Saeed

“… To overcome these limitations, a novel deep space-time generative graph convolutional autoencoder (SGGCA) is proposed…”
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Blockchain-based decentralized smart healthcare using improved wild horse optimizer with Graph Convolutional Autoencoder in IoT environment by Escorcia-Gutierrez, José, Torres-Torres, Melitsa, Soto-Diaz, Roosvel, Soto, Carlos

“…The Internet of Things (IoT) continues to expand by incorporating physical devices, software, computing systems, and hardware that facilitate communication and…”
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Blockchain-Powered Secure and Scalable Threat Intelligence System With Graph Convolutional Autoencoder and Reinforcement Learning Feedback Loop by Khayat, Mohamad, Barka, Ezedin, Adel Serhani, Mohamed, Sallabi, Farag, Shuaib, Khaled, Khater, Heba M.

“…As the field of cybersecurity has experienced continual changes, up-to-date techniques have become increasingly necessary to analyze and defend against…”
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Dynamic graph convolutional autoencoder with node-attribute-wise attention for kidney and tumor segmentation from CT volumes by Ping Xuan, Hui Cui, Hongda Zhang, Tiangang Zhang, Linlin Wang, Toshiya Nakaguchi, Henry B.L. Duh

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