Search Results - Variational graph autoencoder~

Fragment‐based deep molecular generation using hierarchical chemical graph representation and multi‐resolution graph variational autoencoder by Gao, Zhenxiang, Wang, Xinyu, Blumenfeld Gaines, Blake, Shi, Xuetao, Bi, Jinbo, Song, Minghu

“…Graph generative models have recently emerged as an interesting approach to construct molecular structures atom‐by‐atom or fragment‐by‐fragment…”
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VCGAE++: variational collective graph autoEncoder for multi-behavior recommendation: VCGAE++: variational collective graph autoEncoder for multi-behavior by Zhuang, Yingxuan, Liu, Yang, Pan, Weike, Ming, Zhong

“…Variational autoencoder (VAE) is known as a classic and effective method in modeling users…”
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Identification of microbe–disease signed associations via multi-scale variational graph autoencoder based on signed message propagation by Zhu, Huan, Hao, Hongxia, Yu, Liang

“… MSignVGAE employs a graph variational autoencoder to model noisy signed association data and extends the multi-scale concept to enhance representation capabilities…”
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VGAE-CCI: variational graph autoencoder-based construction of 3D spatial cell–cell communication network by Zhang, Tianjiao, Zhang, Xiang, Wu, Zhenao, Ren, Jixiang, Zhao, Zhongqian, Zhang, Hongfei, Wang, Guohua, Wang, Tao

“…Abstract Cell–cell communication plays a critical role in maintaining normal biological functions, regulating development and differentiation, and controlling…”
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Analysis of Training and Seed Bias in Small Molecules Generated with a Conditional Graph-Based Variational Autoencoder─Insights for Practical AI-Driven Molecule Generation by Kang, Seung-Gu, Morrone, Joseph A, Weber, Jeffrey K, Cornell, Wendy D

“… In this work, we analyze the impact of seed and training bias on the output of an activity-conditioned graph-based variational autoencoder (VAE…”
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MAVGAE: a multimodal framework for predicting asymmetric drug-drug interactions based on variational graph autoencoder by Deng, Zengqian, Xu, Jie, Feng, Yinfei, Dong, Liangcheng, Zhang, Yuanyuan

“…Drug-drug interactions refer to the phenomena wherein the potency, duration, or effectiveness of one or multiple drugs undergo alterations of varying degrees…”
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A ^VGAE: An Attribute-Augmented Adversarial Variational Graph Autoencoder for Link Prediction by Pan, Zhihong, Zhong, Zhijie, Wei, Lingling, Lin, Yunxuan, Li, Weisheng, Lin, Ronghua, Tang, Yong

“… variational graph autoencoder (A<inline-formula><tex-math notation="LaTeX">{}^{3}</tex-math></inline-formula>VGAE), which can effectively solve the above two problems…”
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MPI-VGAE: protein–metabolite enzymatic reaction link learning by variational graph autoencoders by Wang, Cheng, Yuan, Chuang, Wang, Yahui, Chen, Ranran, Shi, Yuying, Zhang, Tao, Xue, Fuzhong, Patti, Gary J, Wei, Leyi, Hou, Qingzhen

“…–protein interaction (MPI) prediction are still very limited. In this study, we developed a Variational Graph Autoencoders (VGAE…”
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A Spatial-Temporal Variational Graph Attention Autoencoder Using Interactive Information for Fault Detection in Complex Industrial Processes by Lv, Mingjie, Li, Yonggang, Liang, Huiping, Sun, Bei, Yang, Chunhua, Gui, Weihua

“… A spatial-temporal variational graph attention autoencoder (STVGATE) using interactive information is proposed for fault detection, which aims to effectively capture the spatial and temporal features of the interconnected…”
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Dual stream fusion link prediction for sparse graph based on variational graph autoencoder and pairwise learning by Li, Xun, Cai, Hongyun, Feng, Chuan, Zhao, Ao

“… To address these issues, this paper proposes a novel link prediction method for sparse graphs based on variational graph autoencoder and pairwise learning…”
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A Generative Framework for Predictive Modeling of Multiple Chronic Conditions Using Graph Variational Autoencoder and Bandit-Optimized Graph Neural Network by Rico, Julian Carvajal, Alaeddini, Adel, Faruqui, Syed Hasib Akhter, Fisher-Hoch, Susan P, Mccormick, Joseph B

“… Our framework employs a graph variational autoencoder (GVAE) to capture the complex relationships in patient data…”
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Efficient learning of non-autoregressive graph variational autoencoders for molecular graph generation by Kwon, Youngchun, Yoo, Jiho, Choi, Youn-Suk, Son, Won-Joon, Lee, Dongseon, Kang, Seokho

“… In this paper, we present an improved learning method involving a graph variational autoencoder for efficient molecular graph generation in a non-autoregressive manner…”
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Prediction of microbe–drug associations based on a modified graph attention variational autoencoder and random forest by Wang, Bo, Ma, Fangjian, Du, Xiaoxin, Zhang, Guangda, Li, Jingyou

“… In this study, we developed a computational framework based on a modified graph attention variational autoencoder (MGAVAEMDA…”
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Transformer graph variational autoencoder for generative molecular design by Nguyen, Trieu, Karolak, Aleksandra

“… To address this, we present the transformer graph variational autoencoder (TGVAE), an innovative AI model that employs molecular graphs as input data, thus capturing…”
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Multiresolution equivariant graph variational autoencoder by Hy, Truong Son, Kondor, Risi

“…In this paper, we propose Multiresolution Equivariant Graph Variational Autoencoders (MGVAE…”
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Identifying disease-related microbes based on multi-scale variational graph autoencoder embedding Wasserstein distance by Zhu, Huan, Hao, Hongxia, Yu, Liang

“… Results In this work, we proposed a novel framework, Multi-scale Variational Graph AutoEncoder embedding Wasserstein distance (MVGAEW…”
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Deep graph embedding learning based on multi-variational graph autoencoders for POI recommendation: Deep graph embedding learning by Gong, Weihua, Shen, Genhang, Zhao, Anlun, Yang, Lianghuai, Cheng, Zhen

“… To address this challenge, we propose a new unified heterogeneous graph embedding framework by leveraging multimodal variational graph autoencoders, called MultiVGAE…”
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Deep graph embedding learning based on multi-variational graph autoencoders for POI recommendation by Gong, Weihua, Shen, Genhang, Zhao, Anlun, Yang, Lianghuai, Cheng, Zhen

“… To address this challenge, we propose a new unified heterogeneous graph embedding framework by leveraging multimodal variational graph autoencoders, called MultiVGAE…”
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Dirichlet Process Prior for Student’s t Graph Variational Autoencoders by Zhao, Yuexuan, Huang, Jing

“…Graph variational auto-encoder (GVAE) is a model that combines neural networks and Bayes methods, capable of deeper exploring the influential latent features of graph reconstruction…”
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Importance weighted variational graph autoencoder by Tao, Yuhao, Guo, Lin, Zhao, Shuchang, Zhang, Shiqing

“…Variational Graph Autoencoder (VGAE) is a widely explored model for learning the distribution of graph data…”
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