Machine Learning, Computational Graphs, Generative Adversarial, Deep Learning.
learning models to train from, rather than feeding subgraphs sampled from random walks on a much larger graph as usually done in the literature. Our generated graphs are topologically similar, but is not necessarily a subgraph in the larger graph provided by the data. We compared our approach