Publication-quality Network Visualisations in Python
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.
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
Numerous tutorials, code examples, and a complete documentation of the API can be found on ReadTheDocs.
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)
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},
}
k parameter in get_fruchterman_reingold_layout (issue #79).EditableGraph that occurred when deleting a node while hovering over an edge incident to that node (issue #66).fontsize keyword argument (instead of just size)._shorten_line_by was equal or smaller than zero.InteractiveGraph.EditableGraph (issue #62).EditableGraph (issue #62).step parameter in get_community_layout.bundle_parallel_edges).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.
FAQs
Python drawing utilities for publication quality plots of networks.
We found that netgraph demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Security News
Research
The Socket Research Team breaks down a malicious wrapper package that uses obfuscation to harvest credentials and exfiltrate sensitive data.
Research
Security News
Attackers used a malicious npm package typosquatting a popular ESLint plugin to steal sensitive data, execute commands, and exploit developer systems.
Security News
The Ultralytics' PyPI Package was compromised four times in one weekend through GitHub Actions cache poisoning and failure to rotate previously compromised API tokens.