We propose NetGAN - the first implicit generative model for graphs able to mimic real-world networks. We pose the problem of graph generation as learning the distribution of biased random walks over the input graph. The proposed model is based on a stochastic neural network that generates discrete output samples and is…
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3 results for “NetGAN”
A new method improves NetGAN's random walk initialization for graph generation.
problem NetGAN's random walk initialization leads to inconsistent and variable results, especially with short walks.
method Estimating node importance based on random walk influence over its neighborhood.
result Significantly better accuracy, less variance, and fewer outliers achieved.
MMGAN creates graphs with higher-order motifs for better network simulation.
problem Generative models fail to capture higher-order connectivity patterns in real-world networks.
method Combines multiple biased random walks to capture different motif structures.
result Outperforms NetGAN at creating graphs with accurate network motif statistics.