Before revision:  In particular, we enforce edge-DP by injecting Gaussian noise to the gradients of a link reconstruction based graph generation model, and ensure data utility by improving structure learning with structure-oriented graph comparison . Extensive experiments on two real-world network datasets show that our proposed DPGGEN model is able to generate networks with effectively preserved global structure and rigorously protected individual link privacy.
After revision:  In particular, we enforce edge-DP by injecting Gaussian noise to the gradients of a link reconstruction based graph generation model, and ensure data utility by improving structure learning with structure-oriented graph comparison . Extensive experiments on two real-world network datasets show that our proposed DPGGAN model is able to generate networks with effectively preserved global structure and rigorously protected individual link privacy.