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
datasets. To address this problem, we introduce a novel graph generative model,
Computation Graph Transformer (CGT) that learns and reproduces the distribution
of real-world graphs in a privacy-controlled way. More specifically, CGT (1)
generates effective benchmark graphs on which GNNs show similar task
performance as on the source graphs, (2) scales to process large-scale graphs,
(3) incorporates off-the-shelf privacy modules to guarantee end-user privacy of
the generated graph. Extensive experiments across a vast body of graph
generative models show that only our model can successfully generate
privacy-controlled, synthetic substitutes of large-scale real-world graphs that
can be effectively used to benchmark GNN models.

提出了一种名为 Computation Graph Transformer（CGT）的图形生成模型，它可以在保证隐私的前提下，生成大规模真实世界图的有效基准图，用于作为 Graph Neural Networks 模型的基准测试。