Search Results - variational graph convolutional autoencoder*

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…”
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Anomaly Detection Based on Graph Convolutional Network–Variational Autoencoder Model Using Time-Series Vibration and Current Data by Choi, Seung-Hwan, An, Dawn, Lee, Inho, Lee, Suwoong

“…This paper proposes a deep learning-based anomaly detection method using time-series vibration and current data, which were obtained from endurance tests on…”
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Graph-Variational Convolutional Autoencoder-Based Fault Detection and Diagnosis for Photovoltaic Arrays by Arifeen, Murshedul, Petrovski, Andrei, Hasan, Md Junayed, Noman, Khandaker, Navid, Wasib Ul, Haruna, Auwal

“… This paper introduces a deep learning model that combines a graph convolutional network with a variational autoencoder to diagnose faults in solar arrays…”
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Subject Representation Learning from EEG using Graph Convolutional Variational Autoencoders by Mishra, Aditya, Samin, Ahnaf Mozib, Etemad, Ali, Hashemi, Javad

“…We propose GC-VASE, a graph convolutional-based variational autoencoder that leverages contrastive learning for subject representation learning from EEG data…”
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Optimization of Graph Convolutional Networks with Variational Graph Autoencoder Architecture for 3D Face Reconstruction Task by Batarfi, Mahfoudh M., Mareboyana, Manohar

“… To surmount these obstacles, the study embarks on the optimization of a Variational Graph Autoencoder (VGAE…”
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Graph convolutional network based on self-attention variational autoencoder and capsule contrastive learning for aspect-based sentiment analysis by Wang, Xinyue, Liu, Long, Chen, Zhuo, Wang, Haiyan, Yu, Bin

“… In response to these issues, this article puts forward a hybrid graph convolutional network (GCN…”
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DiffuScope: A diffusion-regularized autoencoder for spatial transcriptomic clustering by Shi, Hua, Yi, Ding, Cui, Yang, Wang, Ruheng, Li, Yan, Ao, Chunyan, Guo, Ruihua, Zhang, Weihang, Peng, Tao, Le, Yuying, Cui, Yaxuan, Wei, Leyi

“… To address this challenge, we propose DiffuScope, a clustering framework based on Graph Convolutional Variational Autoencoders (GC-VAE…”
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GVDTI: graph convolutional and variational autoencoders with attribute-level attention for drug–protein interaction prediction by Hui Cui, Toshiya Nakaguchi, Ping Xuan, Mengsi Fan, Tiangang Zhang

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Scalable Graph Convolutional Variational Autoencoders by Unyi, Daniel, Gyires-Toth, Balint

“… Graph variational autoencoders achieved competitive results on various graph-related modeling tasks (e.g…”
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CVAM: CNA Profile Inference of the Spatial Transcriptome Based on the VGAE and HMM by Ma, Jian, Guo, Jingjing, Fan, Zhiwei, Zhao, Weiling, Zhou, Xiaobo

“…Tumors are often polyclonal due to copy number alteration (CNA) events. Through the CNA profile, we can understand the tumor heterogeneity and consistency. CNA…”
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Handling information loss of graph convolutional networks in collaborative filtering by Xiong, Xin, Li, XunKai, Hu, YouPeng, Wu, YiXuan, Yin, Jian

“… To solve the above problems, we propose Variational AutoEncoder-Enhanced Graph Convolutional Network (VE-GCN) for CF…”
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A broadband oscillation source location method based on LSTM variational autoencoder and graph convolutional neural network by Li, C., Wang, Y., Zheng, Z.

“… Therefore, this paper proposes a wideband oscillation disturbance source localization method based on LSTM variational autoencoder signal compression and graph convolutional neural network…”
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Multi-modal graph convolutional network for vessel trajectory prediction based on cooperative intention enhance using conditional variational autoencoder by Jiang, Junhao, Zuo, Yi, Li, Zhiyuan

“… of trajectory prediction. To address these challenges, we propose a cooperative intention enhance multi-modal graph convolutional network (CIE-MGCN…”
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Design of an Improved Model for Blockchain Forensics Using Graph Convolutional Networks and Variational Autoencoders by Bokade, Sweta A., Sharma, V.K., Manjre, Bhushan M.

“… detection should ideally be present. Current methods struggle to scale with and handle the graph-structured nature of data in blockchains, usually failing in the interpretability that's necessary for either trust or accountability…”
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Recommender Systems Based on Variational Autoencoders and Graph Convolutional Neural Networks by Kang, Peng

“…The high prevalence of online social networks along with the rapid growth of mobile devices makes people have an easier access to large amounts of online…”
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Variational graph auto-encoders for miRNA-disease association prediction by Ding, Yulian, Tian, Li-Ping, Lei, Xiujuan, Liao, Bo, Wu, Fang-Xiang

“…•Graph convolutional networks obtain good representations for miRNAs and diseases.•Variational auto-encoders can deal with missing data in the miRNA-disease network…”
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Graph variational autoencoder with affinity propagation for community-aware anomaly detection in attributed networks by Cao, Zhijie, Yang, Chengkun, Fan, Xiaoqing, Li, Lingjie, Lin, Qiuzhen, Li, Jianqiang, Ma, Lijia

“…) for community-aware ADAN. GVE-AP first employs a graph convolutional variational autoencoder to learn node embeddings from attributed networks…”
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Enhancing microbe-disease association prediction via multi-view graph convolution and latent feature learning by Wang, Bo, Wu, Peilong, Du, Xiaoxin, Zhang, Chunyu, Fu, Shanshan, Sun, Tang, Yang, Xue

“… MVGCVAE is the first model to synergistically integrate multi-view graph convolutional networks (GCNs…”
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MODAPro: Explainable Heterogeneous Networks with Variational Graph Autoencoder for Mining Disease-Specific Functional Molecules and Pathways from Omics Data by Zhao, Jinhui, He, Jiarui, Guan, Pengwei, Bao, Han, Zhao, Xinjie, Zhao, Chunxia, Qin, Wangshu, Lu, Xin, Xu, Guowang

“… To address these critical limitations, we introduce MODAPro, a biologically informed deep learning framework that synergistically integrates variational graph autoencoders (VAE…”
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VHGAE: Drug-Target Interaction Prediction Model Based on Heterogeneous Graph Variational Autoencoder by Zhang, Chen, Sun, Jiaqi, Xing, Linlin, Zhang, Longbo, Cai, Hongzhen, Che, Kai

“… the prediction performance of DTI. Therefore, we propose a method, called VHGAE, based on a heterogeneous graph variational autoencoder to predict drug-target interactions…”
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