netrd

netrd: A library for network reconstruction and graph distances - Published in JOSS (2021)

https://github.com/netsiphd/netrd

Science Score: 95.0%

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Published in Journal of Open Source Software

Scientific Fields

Artificial Intelligence and Machine Learning Computer Science - 83% confidence

Last synced: 4 months ago · JSON representation

Repository

A library for network {reconstruction, distances, dynamics}

Basic Info

Host: GitHub
Owner: netsiphd
License: mit
Language: Python
Default Branch: master
Homepage: https://netrd.readthedocs.io/en/latest/
Size: 62.2 MB

Statistics

Stars: 170
Watchers: 10
Forks: 43
Open Issues: 15
Releases: 5

Created almost 7 years ago · Last pushed almost 3 years ago

Metadata Files

Readme Contributing License

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:

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

example reconstruction

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

```python from netrd.reconstruction import CorrelationMatrix import numpy as np

100 nodes, 1000 observations

TS = np.random.random((100, 1000))

reconstructor = CorrelationMatrix() G = reconstructor.fit(TS, thresholdtype='degree', avgk=15)

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.

Many reconstruction algorithms create a dense matrix of weights and use additional parameters to describe how to create a sparse graph; the tutorial has more details on these parameters.

Distances between graphs

example distance

The basic usage of a distance algorithm is as follows:

```python from netrd.distance import QuantumJSD import networkx as nx G1 = nx.fastgnprandomgraph(1000, .1) G2 = nx.fastgnprandomgraph(1000, .1)

distobj = QuantumJSD() distance = distobj.dist(G1, G2)

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

example distance

The basic usage of a dynamics algorithm is as follows:

```python from netrd.dynamics import VoterModel import networkx as nx groundtruth = nx.karateclub_graph()

dynamicsmodel = VoterModel() syntheticTS = dynamicsmodel.simulate(groundtruth, 1000)

this is the same structure as the input data to a reconstructor

G = CorrelationMatrix().fit(synthetic_TS)

```

This produces a numpy array of time series data.

Contributing

Contributing guidelines can be found in CONTRIBUTING.md.

Publications

McCabe, S., Torres, L., LaRock, T., Haque, S. A., Yang, C.-H., Hartle, H., and Klein, B. (2021). netrd: A library for network reconstruction and graph distances. Journal of Open Source Software 6(62): 2990. doi: 10.21105/joss.02990. arXiv: 2010.16019.
- paper detailing the methods used in this package
Hartle H., Klein B., McCabe S., Daniels A., St-Onge G., Murphy C., and Hébert-Dufresne L. (2020). Network comparison and the within-ensemble graph distance. Proceedings of the Royal Society A 476: 20190744. doi: 10.1098/rspa.2019.0744. arXiv: 2008.02415.
- recent work introducing a baseline measure for comparing graph distances

Owner

Name: NetSI PhD Students
Login: netsiphd
Kind: user

Website: http://networkscienceinstitute.org/
Repositories: 6
Profile: https://github.com/netsiphd

JOSS Publication

netrd: A library for network reconstruction and graph distances

Published

June 01, 2021

DOI

10.21105/joss.02990

Volume 6, Issue 62, Page 2990