# Can NetGAN be improved on short random walks?

## TL;DR

This paper proposes a new method for initializing random walks in NetGAN, which improves graph generation accuracy and consistency, especially for short walks, by estimating node importance based on local influence.

## Contribution

It introduces a novel node importance estimation method for initializing random walks in NetGAN, enhancing performance and stability over traditional random start approaches.

## Key findings

- Significantly improved accuracy in graph generation.
- Reduced variance and outliers in results.
- Better performance with short random walks.

## Abstract

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.