Optimizing Variational Graph Autoencoder for Community Detection with Dual Optimization [PDF]
Entropy, 2020 Variational Graph Autoencoder (VGAE) has recently gained traction for learning representations on graphs. Its inception has allowed models to achieve state-of-the-art performance for challenging tasks such as link prediction, rating prediction, and node ...
Jun Jin Choong +2 more
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Interpretable Variational Graph Autoencoder with Noninformative Prior [PDF]
Future Internet, 2021 Variational graph autoencoder, which can encode structural information and attribute information in the graph into low-dimensional representations, has become a powerful method for studying graph-structured data.
Bo Yang, Lili Sun, Xueyan Liu
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Nearest Neighbours Graph Variational AutoEncoder
Algorithms, 2023 Graphs are versatile structures for the representation of many real-world data. Deep Learning on graphs is currently able to solve a wide range of problems with excellent results.
Carlo Mancini-Terracciano +2 more
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Adversarial Attention-Based Variational Graph Autoencoder [PDF]
IEEE Access, 2020 Autoencoders have been successfully used for graph embedding, and many variants have been proven to effectively express graph data and conduct graph analysis in low-dimensional space.
Wei Dou, Weiyu Zhang, Ziqiang Weng
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Importance weighted variational graph autoencoder
Complex & Intelligent SystemsVariational Graph Autoencoder (VGAE) is a widely explored model for learning the distribution of graph data. Currently, the approximate posterior distribution in VGAE-based methods is overly restrictive, leading to a significant gap between the ...
Shiqing Zhang, Zhang Shiqing
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Multiresolution equivariant graph variational autoencoder
Machine Learning: Science and Technology, 2023 In this paper, we propose Multiresolution Equivariant Graph Variational Autoencoders (MGVAE), the first hierarchical generative model to learn and generate graphs in a multiresolution and equivariant manner. At each resolution level, MGVAE employs higher
Truong Son Hy, Risi Kondor
doaj +6 more sources
Identifying disease-related microbes based on multi-scale variational graph autoencoder embedding Wasserstein distance [PDF]
BMC Biology, 2023 Background Enormous clinical and biomedical researches have demonstrated that microbes are crucial to human health. Identifying associations between microbes and diseases can not only reveal potential disease mechanisms, but also facilitate early ...
Huan Zhu, Hongxia Hao, Liang Yu
doaj +2 more sources
Transformer and graph variational autoencoder to identify microenvironments: A deep learning protocol for spatial transcriptomics [PDF]
STAR ProtocolsSummary: We present transformer and graph variational autoencoder to identify microenvironments (TG-ME), a computational framework that integrates transformer and graph variational autoencoders to dissect spatial niches using spatial transcriptomics and ...
Karla Paniagua +5 more
doaj +2 more sources
Frontiers in MicrobiologyIntroductionThe identification of microbe–drug associations can greatly facilitate drug research and development. Traditional methods for screening microbe-drug associations are time-consuming, manpower-intensive, and costly to conduct, so computational ...
Bo Wang +6 more
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Identification of microbe–disease signed associations via multi-scale variational graph autoencoder based on signed message propagation [PDF]
BMC BiologyBackground Plenty of clinical and biomedical research has unequivocally highlighted the tremendous significance of the human microbiome in relation to human health.
Huan Zhu, Hongxia Hao, Liang Yu
doaj +2 more sources