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Scalable and Privacy-enhanced Graph Generative Model for Graph Neural Networks. (arXiv:2207.04396v2 [cs.LG] UPDATED)
Oct. 13, 2022, 1:26 a.m. | Minji Yoon, Yue Wu, John Palowitch, Bryan Perozzi, Ruslan Salakhutdinov
cs.CR updates on arXiv.org arxiv.org
As the field of Graph Neural Networks (GNN) continues to grow, it experiences
a corresponding increase in the need for large, real-world datasets to train
and test new GNN models on challenging, realistic problems. Unfortunately, such
graph datasets are often generated from online, highly privacy-restricted
ecosystems, which makes research and development on these datasets hard, if not
impossible. This greatly reduces the amount of benchmark graphs available to
researchers, causing the field to rely only on a handful of publicly-available …
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