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Semi-deterministic and Contrastive Variational Graph Autoencoder for Recommendation
Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 2021Variational AutoEncoder (VAE) is a popular deep generative framework with a solid theoretical basis. There are many research efforts on improving VAE. Among the existing works, a recently proposed deterministic Regularized AutoEncoder (RAE) provides a new scheme for generative modeling.
Yue Ding 0001 +7 more
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Optimizing Variational Graph Autoencoder for Community Detection
2019 IEEE International Conference on Big Data (Big Data), 2019Variational Graph Autoencoders (VGAE) has recently been a popular framework of choice for learning representations on graphs. Its inception has allowed models to achieve state-of-the-art performances for challenging tasks such as link prediction, rating prediction and node clustering.
Jun Jin Choong +2 more
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Demystifying Tax Evasion Using Variational Graph Autoencoders
2021Indirect taxation is a significant source of income for any nation. Tax evasion hinders the progress of a nation. It causes a substantial loss to the revenue of a country. We design a model based on variational graph autoencoders and clustering to identify taxpayers who are evading indirect tax by providing false information in their tax returns.
Priya Mehta +3 more
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International Journal of Pattern Recognition and Artificial Intelligence, 2022
Graph neural network (GNN) is a powerful representation learning framework for graph-structured data. Some GNN-based graph embedding methods, including variational graph autoencoder (VGAE), have been presented recently. However, existing VGAE-based methods typically focus on reconstructing the adjacent matrix, i.e.
Dongjie Li, Dong Li, Guang Lian
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Graph neural network (GNN) is a powerful representation learning framework for graph-structured data. Some GNN-based graph embedding methods, including variational graph autoencoder (VGAE), have been presented recently. However, existing VGAE-based methods typically focus on reconstructing the adjacent matrix, i.e.
Dongjie Li, Dong Li, Guang Lian
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Regularizing Variational Autoencoders for Molecular Graph Generation
2019Deep generative models for graphs are promising for being able to sidestep expensive search procedures in the huge space of chemical compounds. However, incorporating complex and non-differentiable property metrics into a generative model remains a challenge.
Xin Li +4 more
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Graph Embedding For Link Prediction Using Residual Variational Graph Autoencoders
2020 28th Signal Processing and Communications Applications Conference (SIU), 2020Graphs are usually represented by high dimensional data. Hence, graph embedding is an essential task, which aims to represent a graph in a lower dimension while protecting the original graph's properties. In this paper, we propose a novel graph embedding method called Residual Variational Graph Autoencoder (RVGAE), which boosts variational graph ...
Reyhan Kevser Keser +4 more
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Variational Type Graph Autoencoder for Denoising on Event Recommendation
ACM Transactions on Information SystemsRecommendations for events play a pivotal role in facilitating the discovery of upcoming intriguing events within Event-Based Social Networks (EBSNs). Previous research has established the crucial significance of mining contextual features and implicit relationships to enhance recommendation performance and alleviate data sparsity issues.
Shuo Zhang, Xiangwu Meng, Yujie Zhang
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RecGraph: Graph Recovery Attack using Variational Graph Autoencoders
2021 IEEE International Performance, Computing, and Communications Conference (IPCCC), 2021Jing Tian +5 more
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MoVAE: A Variational AutoEncoder for Molecular Graph Generation
2023Zerun Lin +4 more
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Adversarial Residual Variational Graph Autoencoder with Batch Normalization
2021 IEEE Sixth International Conference on Data Science in Cyberspace (DSC), 2021Qisheng Liao +5 more
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