  Graphs are useful structures that can model several important real-world
problems. Recently, learning graphs have drawn considerable attention, leading
to the proposal of new methods for learning these data structures. One of these
studies produced NetGAN, a new approach for generating graphs via random walks.
Although NetGAN has shown promising results in terms of accuracy in the tasks
of generating graphs and link prediction, the choice of vertices from which it
starts random walks can lead to inconsistent and highly variable results,
especially when the length of walks is short. As an alternative to random
starting, this study aims to establish a new method for initializing random
walks from a set of dense vertices. We purpose estimating the importance of a
node based on the inverse of its influence over the whole vertices of its
neighborhood through random walks of different sizes. The proposed method
manages to achieve significantly better accuracy, less variance and lesser
outliers.

Machine Learning;Graphics;Machine Learning