This repository contains the code for generating dynamic networks. Dynamic networks are networks that grow one node at a time. The new node first samples its random out-degree, i.e. the number of existing nodes it will attach too. In this repository, this is sampled from the empirical out-degree distribution, which is stored as a python dictionary in out_degree_distr.py. The node then attaches to existing nodes, with probabilities defined by a given combinations of the following mechanisms:
Combinations of these mechanisms leads to $2 \times 2 \times 4 = 16$ different possible generative models. However, certain combinations have nice properties (e.g. scale-free) while others have not-so-nice properties (e.g. being explosive in size).
In addition, this repository contains code for:
The simulation method builds a continuous time branching process. This is a tree. The tree is of size $\text{network size} \times \text{sum of out-degrees}$. The tree is then collapsed to form the final network.
The repository is structured as follows:
simulation.py contains the main simulation.
empirical_distribution_outdegree.py for sampling the empirical outdegree of each vertex, which in turn uses out_degree_distr_OC.pkl as data.generate_dataset.py contains the script that was used to generate the networks in the paper.dynamic_feature_extraction.py contains code for loading the dynamic network and calculating the dynamic feature matrices as specified in the paper.
static_feature_extraction.py contains code for calculating the static features used in the paper.
dynamic_feature_extraction.py to load the dynamic network and get the static final snapshot as a networkx Graph object.calculating_static_featuers_with_graph_tool.py.