Search Results - Heterogeneous graph autoencoder

NodeHGAE: Node-oriented heterogeneous graph autoencoder by Zhu, Xiangkai, Li, Chao, Yan, Yeyu, Zhao, Zhongying, Duan, Hua, Zeng, Qingtian

“…Heterogeneous graph autoencoder (HGAE), as an unsupervised learning approach, aims to encode nodes and edges of heterogeneous graphs into low-dimensional vector representations, and simultaneously reconstruct the original graph structure…”
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Conditional Enhanced Variational Autoencoder-Heterogeneous Graph Attention Neural Network: A Novel Fault Diagnosis Method for Electric Rudders Based on Heterogeneous Information by Cao, Ximing, Yang, Ruifeng, Guo, Chenxia, Qin, Hao

“…In machine fault diagnosis, despite the wealth of information multi-sensor data provide for constructing high-quality graphs, existing graph data-driven diagnostic methods face challenges posed…”
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Hgae: Heterogeneous Graph Autoencoder-Based Service Bundle Recommendations for Efficient Mashup Development by Sun, Kaipu, Wang, Xuanye, Xi, Meng, Wu, Yangyang, Pan, Xiaohua, Zhang, Jinshan, Li, Ying, Ma, Kun, Yin, Jianwei

“… In this work, we propose an innovative message-passing model, a Heterogeneous Graph AutoEncoderbased service bundle recommendation model (HGAE…”
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Deceptive reviewer group detection using self-adversarial variational autoencoder: a heterogeneous graph-based approach by Maurya, Sushil Kumar, Singh, Dinesh

“… by completing the user-review-product graph. To accomplish this, we propose an integrated approach comprising three key components…”
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V-GMR: a variational autoencoder-based heterogeneous graph multi-behavior recommendation model by Yang, Haoqin, Rang, Ran, Xing, Linlin, Zhang, Longbo, Cai, Hongzhen, Guo, Maozu, Sun, Jiaqi

“… In this paper, we propose a variational autoencoder (VAE) and graph-based heterogeneous multibehavior recommendation model (V-GMR…”
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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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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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GIAE-DTI: Predicting Drug-Target Interactions Based on Heterogeneous Network and GIN-Based Graph Autoencoder by Wang, Mengdi, Lei, Xiujuan, Liu, Lian, Chen, Jianrui, Wu, Fang-Xiang

“…-modal similarity of drugs and targets and constructs a heterogeneous network for DTI prediction…”
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SELECTOR: Heterogeneous graph network with convolutional masked autoencoder for multimodal robust prediction of cancer survival by Pan, Liangrui, Peng, Yijun, Li, Yan, Wang, Xiang, Liu, Wenjuan, Xu, Liwen, Liang, Qingchun, Peng, Shaoliang

“… This paper introduces SELECTOR, a heterogeneous graph-aware network based on convolutional mask encoders for robust multimodal prediction of cancer patient survival…”
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GADTI: Graph Autoencoder Approach for DTI Prediction From Heterogeneous Network by Liu, Zhixian, Chen, Qingfeng, Lan, Wei, Pan, Haiming, Hao, Xinkun, Pan, Shirui

“… In this article, a graph autoencoder approach for DTI prediction (GADTI) was proposed to discover potential interactions between drugs and targets using a heterogeneous network, which integrates diverse drug-related and target-related datasets…”
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Drug repositioning based on heterogeneous networks and variational graph autoencoders by Lei, Song, Lei, Xiujuan, Liu, Lian

“… years has facilitated drug development. In this study we propose a drug repositioning method, VGAEDR, based on a heterogeneous network of multiple drug attributes and a variational graph autoencoder…”
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Predicting potential microbe-disease associations based on heterogeneous graph attention network and deep sparse autoencoder by Wang, Bo, Zhao, Wenlong, Du, Xiaoxin, Zhang, Jianfei, Zhang, Chunyu, Wang, Liping, He, Yang

“… We propose a computational framework called graph attention convolutional deep sparse autoencoder microbe-disease association (GCDSAEMDA…”
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AEGCN: An Autoencoder-Constrained Graph Convolutional Network by Ma, Mingyuan, Na, Sen, Wang, Hongyu

“… We consider applying our model on both homogeneous graphs and heterogeneous graphs. For homogeneous graphs, the autoencoder approximates to the adjacency matrix…”
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Multi-Head Graph Attention Adversarial Autoencoder Network for Unsupervised Change Detection Using Heterogeneous Remote Sensing Images by Jia, Meng, Lou, Xiangyu, Zhao, Zhiqiang, Lu, Xiaofeng, Shi, Zhenghao

“… To address this issue, we propose the Multi-Head Graph Attention Mechanism (MHGAN), designed to achieve accurate detection of surface changes in heterogeneous remote sensing images…”
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Heterogeneous Graph Masked Autoencoders by Tian, Yijun, Dong, Kaiwen, Zhang, Chunhui, Zhang, Chuxu, Chawla, Nitesh V

“…? In light of this, we study the problem of generative SSL on heterogeneous graphs and propose HGMAE, a novel heterogeneous graph masked autoencoder model to address these challenges…”
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Variational Graph Autoencoder for Heterogeneous Information Networks with Missing and Inaccurate Attributes by Zhao, Yige, Yu, Jianxiang, Cheng, Yao, Yu, Chengcheng, Liu, Yiding, Li, Xiang, Wang, Shuaiqiang

“…Heterogeneous Information Networks (HINs), which consist of various types of nodes and edges, have recently demonstrated excellent performance in graph mining…”
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Exploring Microbe-Drug Association Prediction via Multi-Attribute Dual-Decoder Graph Autoencoder by Liu, Wei, Deng, Xiangcheng, Sun, Xingen, Lu, Xu, Chen, Xing

“… In this work, we propose a method called exploring microbe-drug association prediction via multi-attribute dual-decoder graph autoencoder (MDGAEMDA…”
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Dataset Recommendation via Variational Graph Autoencoder by Altaf, Basmah, Akujuobi, Uchenna, Yu, Lu, Zhang, Xiangliang

“…This paper targets on designing a query-based dataset recommendation system, which accepts a query denoting a user's research interest as a set of research…”
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GSAMDA: a computational model for predicting potential microbe–drug associations based on graph attention network and sparse autoencoder by Tan, Yaqin, Zou, Juan, Kuang, Linai, Wang, Xiangyi, Zeng, Bin, Zhang, Zhen, Wang, Lei

“… Results In this work, we proposed a novel computational model named GSAMDA based on the graph attention network and sparse autoencoder to infer latent microbe–drug associations…”
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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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