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
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 .
Aside from NetworkX and the Python scientific computing stack, this library also has dependencies on Cython and POT.
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.
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.
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 guidelines can be found in CONTRIBUTING.md.