Machine Learning, Computational Graphs, Generative Adversarial, Deep Learning.
the graph as positive samples and generate unlinked pairs as negative samples for the validation set. The remaining 90% of the edges is used for training. Results The below figure shows the validation accuracy versus time spent for the experiment on the social network. Our method versus GraphGAN on