Conditional Structure Generation through Graph Variational Generative Adversarial Nets

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On the Binding Problem in Artificial Neural Networks (2020, background): “, 2019), or GANs (Yang et al., 2019).”
Subgraph Federated Learning with Missing Neighbor Generation (2021, methodology): “Similar to most FL methods, it is originally proposed for traditional machine learning problems (Yang et al., 2019b) to allow collaborative training on silo data through local updating and global aggregation.”
Secure Deep Graph Generation with Link Differential Privacy (2020, background): “(6)), regarding a suite of graph statistics commonly used to evaluate the performance of graph generation models, especially from a global perspective [Bojchevski et al., 2018; You et al., 2018; Yang et al., 2019].”
Deep Set Prediction Networks (2019, background): “[26] compute a permutation-invariant loss between graphs by comparing them in the latent space (similar to our Lrepr) in an adversarial setting.”
Graph Representation Learning in Biomedicine and Healthcare (2021, unknown): “Additionally, applying GCNs, variational autoencoders and generative adversarial networks jointly to gene-correlation networks (initialized with a subset of gene-expression matrices) can generate disease networks with the desired properties 134 .”