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paulbrodersen / netgraph

Publication-quality network visualisations in python
GNU General Public License v3.0
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graph graph-tool igraph matplotlib network network-analysis network-science networkx publication-quality-plots python visualization

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Netgraph

Publication-quality Network Visualisations in Python

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Netgraph is a Python library that aims to complement existing network analysis libraries such as such as networkx, igraph, and graph-tool with publication-quality visualisations within the Python ecosystem. To facilitate a seamless integration, Netgraph supports a variety of input formats, including networkx, igraph, and graph-tool Graph objects. Netgraph implements numerous node layout algorithms and several edge routing routines. Uniquely among Python alternatives, it handles networks with multiple components gracefully (which otherwise break most node layout routines), and it post-processes the output of the node layout and edge routing algorithms with several heuristics to increase the interpretability of the visualisation (reduction of overlaps between nodes, edges, and labels; edge crossing minimisation and edge unbundling where applicable). The highly customisable plots are created using Matplotlib, and the resulting Matplotlib objects are exposed in an easily queryable format such that they can be further manipulated and/or animated using standard Matplotlib syntax. Finally, Netgraph also supports interactive changes: with the InteractiveGraph class, nodes and edges can be positioned using the mouse, and the EditableGraph class additionally supports insertion and deletion of nodes and edges as well as their (re-)labelling through standard text-entry.

Installation

Install the current release of netgraph from PyPI:

pip install netgraph

If you are using (Ana-)conda (or mamba), you can also obtain Netgraph from conda-forge:

conda install -c conda-forge netgraph

Documentation

Numerous tutorials, code examples, and a complete documentation of the API can be found on ReadTheDocs.

Quickstart

import matplotlib.pyplot as plt
from netgraph import Graph, InteractiveGraph, EditableGraph

# Several graph formats are supported:

# 1) edge lists
graph_data = [(0, 1), (1, 2), (2, 0)]

# 2) edge list with weights
graph_data = [(0, 1, 0.2), (1, 2, -0.4), (2, 0, 0.7)]

# 3) full rank matrices
import numpy
graph_data = np.random.rand(10, 10)

# 4) networkx Graph and DiGraph objects (MultiGraph objects are not supported, yet)
import networkx
graph_data = networkx.karate_club_graph()

# 5) igraph.Graph objects
import igraph
graph_data = igraph.Graph.Famous('Zachary')

# 6) graph_tool.Graph objects
import graph_tool.collection
graph_data = graph_tool.collection.data["karate"]

# Create a non-interactive plot:
Graph(graph_data)
plt.show()

# Create an interactive plot, in which the nodes can be re-positioned with the mouse.
# NOTE: you must retain a reference to the plot instance!
# Otherwise, the plot instance will be garbage collected after the initial draw
# and you won't be able to move the plot elements around.
# For related reasons, if you are using PyCharm, you have to execute the code in
# a console (Alt+Shift+E).
plot_instance = InteractiveGraph(graph_data)
plt.show()

# Create an editable plot, which is an interactive plot with the additions
# that nodes and edges can be inserted or deleted, and labels and annotations
# can be created, edited, or deleted as well.
plot_instance = EditableGraph(graph_data)
plt.show()

# Netgraph uses Matplotlib for creating the visualisation.
# Node and edge artistis are derived from `matplotlib.patches.PathPatch`.
# Node and edge labels are `matplotlib.text.Text` instances.
# Standard matplotlib syntax applies.
fig, ax = plt.subplots(figsize=(5,4))
plot_instance = Graph([(0, 1)], node_labels=True, edge_labels=True, ax=ax)
plot_instance.node_artists[0].set_alpha(0.2)
plot_instance.edge_artists[(0, 1)].set_facecolor('red')
plot_instance.edge_label_artists[(0, 1)].set_style('italic')
plot_instance.node_label_artists[1].set_size(10)
ax.set_title("This is my fancy title.")
ax.set_facecolor('honeydew') # change background color
fig.canvas.draw() # force redraw to display changes
fig.savefig('test.pdf', dpi=300)
plt.show()

# Read the documentation for a full list of available arguments:
help(Graph)
help(InteractiveGraph)
help(EditableGraph)

Examples

Example visualisations

Citing Netgraph

If you use Netgraph in a scientific publication, I would appreciate citations to the following paper:

Brodersen, P. J. N., (2023). Netgraph: Publication-quality Network Visualisations in Python. Journal of Open Source Software, 8(87), 5372, https://doi.org/10.21105/joss.05372

Bibtex entry:

@article{Brodersen2023,
    doi     = {10.21105/joss.05372},
    url     = {https://doi.org/10.21105/joss.05372},
    year    = {2023}, publisher = {The Open Journal},
    volume  = {8},
    number  = {87},
    pages   = {5372},
    author  = {Paul J. N. Brodersen},
    title   = {Netgraph: Publication-quality Network Visualisations in Python},
    journal = {Journal of Open Source Software},
}

Recent changes

Help, I don't know how to do ...!

Please raise an issue. Include any relevant code and data in a minimal, reproducible example. If applicable, make a sketch of the desired result with pen and paper, take a picture, and append it to the issue.

Bug reports are, of course, always welcome. Please make sure to include the full error trace.

If you submit a pull request that fixes a bug or implements a cool feature, I will probably worship the ground you walk on for the rest of the week. Probably.

Finally, if you do email me, please be very patient. I rarely check the email account linked to my open source code, so I probably will not see your emails for several weeks, potentially longer. Also, I have a job that I love and that pays my bills, and thus takes priority. That being said, the blue little notification dot on GitHub is surprisingly effective at getting my attention. So please just raise an issue.