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Variational Graph Autoencoders (VGAEs) are powerful models for unsupervised learning of node representations from graph data.
The hungarian method for the assignment problem
Harold W Kuhn · 1955
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Stochastic blockmodels for directed graphs
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Methods and metrics for cold-start recommendations
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Immunology of type 1 diabetes
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Stick-breaking construction for the indian buffet process
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Joint latent topic models for text and citations
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Overlapping stochastic block models with application to the french political blogosphere
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Generative adversarial nets
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Adam: A method for stochastic optimization
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Deepwalk: Online learning of social representations
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Coronary artery disease in type 2 diabetes mellitus: Recent treatment strategies and future perspectives
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Stochastic blockmodels meet graph neural networks
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Learning graph embedding with adversarial training methods
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On the equivalence between positional node embeddings and structural graph representations
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Rethinking kernel methods for node representation learning on graphs
Yu Tian, Long Zhao, Xi Peng, and Dimitris Metaxas · 2019
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Open graph benchmark: Datasets for machine learning on graphs
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec · 2020
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Tudataset: A collection of benchmark datasets for learning with graphs
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Graph representation learning via ladder gamma variational autoencoders
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Graph neural collaborative topic model for citation recommendation
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