This repository contains the source code and (generated) datasets used in the CNSM2022 paper Comparing Traditional and GAN-based Approaches for the Synthesis of Wide Area Network Topologies. Below is an overview of the contained folders and files. [More info below to come :)]
These archives contain the generated networks for all of the investigated generators.
For the traditional generations (2K, ER, BA, WS), it contains 1000 files each in the following format:
synth_sample_<generator>_ i.pkl, which are NetworkX graphs (nx.Graph()).
For the GAN, the contained data is more complex. First, there are files named image_at_epoch_1000_s_(BW|RGB).pdf, which showcase samples of the generated networks for each of the then seeds s, just for some visualization, other than that, they have no further use.
Furthermore, for the GAN, there are files named sample_at_epoch_1000_s_i_(BW|RGB).pkl, which are the raw files that the GAN produced with seed s. These are NumPy Arrays (np.array()) with dimensions n x n x 1 for BW and n x n x 3 for RGB.
Next and last, there are files named synth_sample_i_s_weightssampled?_(BW|RGB).pkl. These are the postprocessed samples for the GAN, for seed s and weightssampled? is a boolean (so, 0 or 1), which specifies if the weights were sampled onto the graph. Like for the traditional generators, these are NetworkX graphs (nx.Graph()).