Scalable and Privacy-enhanced Graph Generative Model for Graph Neural Networks

Minji Yoon, Yue Wu, John Palowitch, Bryan Perozzi, Russ Salakhutdinov

Keywords: graph generative model, graph neural networks, graph convolutional networks, benchmark graph generation

TL;DR: We propose a novel, modern graph generation problem to enable generating privacy-controlled, synthetic substitutes of large-scale real-world graphs that can be effectively used to evaluate GNN models.

Abstract: 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 dilemma, we introduce a novel graph generative model, Computation Graph Transformer (CGT) that can learn and reproduce the distribution of real-world graphs in a privacy-enhanced way. Our proposed model (1) generates effective benchmark graphs on which GNNs show similar task performance as on the source graphs, (2) scales to process large-scale real-world graphs, (3) guarantees privacy for end-users. 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 evaluate GNN models.

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