gosiatr/netrd

A library for network {reconstruction, distances, dynamics}

netrd: A library for network {reconstruction, distances, dynamics}

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.

Some resources that maybe of interest:

• An interactive demonstration: netrd explorer
• A tutorial on how to use the library
• The API reference
• A notebook showing advanced usage

Installation

netrd is easy to install through pip:

pip install netrd

If you are thinking about contributing to netrd, you can install a development version by executing

git clone https://github.com/netsiphd/netrd
cd netrd
pip install .

Usage

Reconstructing a graph

The basic usage of a graph reconstruction algorithm is as follows:

>>> reconstructor = ReconstructionAlgorithm()
>>> G = reconstructor.fit(TS, <some_params>)
>>> # or alternately, G = reconstructor.results['graph']

Here, TS is an N x L numpy array consisting of L observations for each of N sensors. This constrains the graphs to have integer-valued nodes.

The results dict object, in addition to containing the graph object, may also contain objects created as a side effect of reconstructing the network, which may be useful for debugging or considering goodness of fit. What is returned will vary between reconstruction algorithms.

Distances between graphs

The basic usage of a distance algorithm is as follows:

>>> dist_obj = DistanceAlgorithm()
>>> distance = dist_obj.dist(G1, G2, <some_params>)
>>> # or alternatively: distance = dist_obj.results['dist']

Here, G1 and G2 are nx.Graph objects (or subclasses such as nx.DiGraph). The results dictionary holds the distance value, as well as any other values that were computed as a side effect.

Dynamics on graphs

The basic usage of a dynamics algorithm is as follows:

>>> ground_truth = nx.read_edgelist("ground_truth.txt")
>>> dynamics_model = Dynamics()
>>> synthetic_TS = dynamics_model.simulate(ground_truth, <some_params>)
>>> # G = Reconstructor().fit(synthetic_TS)

This produces a numpy array of time series data.

Contributing

Contributing guidelines can be found in CONTRIBUTING.md.