DynamicRandomGraphs: A Python package for generation of scalable temporal random graphs

About The Project

DynamicRandomGraphs provides implementations of dynamic networks in Python. It deals with the efficient generation of scalable dynamic (temporal) random networks.

The package was first used to simulate the spread of Covid-19 under different social distancing conditions as presented in Networks 2021 and in Arxiv paper titled: An interaction-based contagion model over temporal networks demonstrates that reducing temporal network density reduces total infection rate. When using the package please cite the Arxiv paper: Abbey, A., Marmor, Y., Shahar, Y., Mokryn, O. (2022). An interaction-based contagion model over temporal networks demonstrates that reducing temporal network density reduces total infection rate. ArXiv 2202.11591, 1–21. https://arxiv.org/abs/2202.11591

The talk at Networks2021: Reducing temporal density reduces total infection rate

Getting started

Prerequisites

Make sure you have Python3 installed on your computer, as well as pip and setuptools, and that they are up to date. If you want to be safe, take a look at the guide to installing Python packages.

Clone the codebase from this GitHub repository with the small green icon on the up-right corner Code or from the terminal:

git clone ScanLab-ossi/random_dynamic_graph

Then, install the required packages:

 pip install -r requirements.txt

Usage

Our package operates in two modes. A stand-alone mode allows for the generation of a series of graphs representing the temporal network of choice; The parallel mode creates a set of temporal networks, each consisting of multiple graphs, according to a predefined set of parameters.

Stand-alone mode

python -m random_graph_generator --node=100 --steps=200 --mode="pandas"

This command will create network with 100 nodes along 200 time steps. That information will be saved as CSV file.

Similar example will create NetworkX object, and the relevant command is:

python -m random_graph_generator --node=100 --steps=200 --mode="NetworkX"

Other parameters are accessible for that mode:

Parameters
        ----------
        n : int
            The number of nodes.
        steps: int
            The number of timestamps in the temporal network. The length of the network in the time axis.
        up_rate : float
            Probability for edge creation.
        down_rate: float
            Probability of edge removal
        write_to_csv: bool, optional (default=False)
            False will return pandas dataframe, True will write the results to CSV (preferred for huge networks)
        output_file_name : str
            Destination for csv file, relevant only if ''write_to_csv'' is True
        is_directed : bool, optional (default=False)
            If True, this function returns a directed graph.

Parallel mode

Many networks can be created in parallel in this mode of operation. To do so, use the JSON file as the ` conf_example.json``.

python -m random_graph_generator --config="conf_example.json"

The json example looks like:

{
  "nodes": [
    100,
    200,
    500
  ],
  "steps": [
    1000,
    2000
  ],
  "up": [
    0.001,
    0.005
  ],
  "down": [
    0.2,
    0.4
  ],
  "is_directed": [
    false
  ]
}

The generator makes a graph from every combination of parameters in the lists. From the example above, the generator creates 24 different networks.

License

Distributed under the MIT License. See LICENSE for more information.

Cite: Abbey, A., Marmor, Y., Shahar, Y.,Mokryn, O. (2022). An interaction-based contagion model over temporal networks demonstrates that reducing temporal network density reduces total infection rate. ArXiv 2202.11591, 1–21. https://arxiv.org/abs/2202.11591

Roadmap

See the open issues for a list of proposed features (and known issues).

In general, we would like to extend the model for better control on the parameters on the time-line and on the degrees distribution and communities parameters as well.

Contributing

Feel free to fork and help out.

Test using pytest. Run slower tests with pytest --runslow.

Related works

• NetworkX - a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks (Great package, but not suitable for large networks or temporal networks).
• DyNetx - a Python software package that extends NetworkX with dynamic network models and algorithms (Excellent package, but performance in large-scale temporal networks is limited) .
• DANCer - a Scala software that generates dynamic attributed networks with community structure that follow the known properties of real-world networks (paper).
• pathpy - an Open Source python package providing higher-order network analytics for time series data.
• TACOMA - TemporAl COntact Modeling and Analysis. Provides fast tools to analyze temporal contact networks, produce surrogate networks using qualitative models and simulate Gillespie processes on them. TACOMA is a joint C++/Python-package for the modeling and analysis of undirected and unweighted temporal networks, with a focus on (but not limited to) human face-to-face contact networks.
• Teneto - a Python package includes various tools for analyzing temporal network data. Temporal network measures, temporal network generation, derivation of time-varying/dynamic connectivity, plotting functions.
• netrd - This library provides a consistent, NetworkX-based interface to various utilities for graph distances, graph reconstruction from time series data, and simulated dynamics on networks.
• Random Modular Network Generator Generates random graphs with tunable strength of community structure
• randomGraph very simple random graph generator in matlab
• Graph1 Random Graph Generator with Max capacity paths (C++)
• grapherator - The R package grapherator implements a modular approach to benchmark graph generation focusing on undirected, weighted graphs.
• igraph: Network Analysis and Visualization This R package Includes some methods to generate classical Erdos-Renyi random graphs as well as more recent models, e.g., small-world graphs.
• netgen: Network Generator for Combinatorial Graph Problems This R package Contains some methods to generate complete graphs especially for benchmarking Travelling-Salesperson-Problem solvers.
• bnlearn: Bayesian Network Structure Learning, Parameter Learning and Inference The R function bnlearn::random.graph implements some algorithms to create graphs.

How to report issues?

Running into any bugs? Check out the open issues to see if we're already working on it. If not, open up a new issue, and we will check it out when we can. Thank you.