python-igraph Manual

For using igraph from Python

Visualisation of graphs

Visualisation of graphs

igraph includes functionality to visualize graphs. There are two main components: graph layouts and graph plotting.

In the following examples, we will assume igraph is imported as ig and a Graph object has been previously created, e.g.:

>>> import igraph as ig
>>> g = ig.Graph(edges=[[0, 1], [2, 3]])

Read the API documentation for details on each function and class. The tutorial contains examples to get started.

Graph layouts

A graph layout is a low-dimensional (usually: 2 dimensional) representation of a graph. Different layouts for the same graph can be computed and typically preserve or highlight distinct properties of the graph itself. Some layouts only make sense for specific kinds of graphs, such as trees.

igraph offers several graph layouts. The general function to compute a graph layout is Graph.layout():

>>> layout = g.layout(layout='auto')

See below for a list of supported layouts. The resulting object is an instance of igraph.layout.Layout and has some useful properties:

  • Layout.coords: the coordinates of the vertices in the layout (each row is a vertex)

  • Layout.dim: the number of dimensions of the embedding (usually 2)

and methods:

  • Layout.boundaries() the rectangle with the extreme coordinates of the layout

  • Layout.bounding_box() the boundary, but as an igraph.drawing.utils.BoundingBox (see below)

  • Layout.centroid() the coordinates of the centroid of the graph layout

Indexing and slicing can be performed and returns the coordinates of the requested vertices:

>>> coords_subgraph = layout[:2]  # Coordinates of the first two vertices


The returned object is a list of lists with the coordinates, not an igraph.layout.Layout object. You can wrap the result into such an object easily:

>>> layout_subgraph = ig.Layout(coords=layout[:2])

It is possible to perform linear transformations to the layout:

  • Layout.translate()


  • Layout.scale()

  • Layout.fit_into()

  • Layout.rotate()

  • Layout.mirror()

as well as a generic nonlinear transformation via:

  • Layout.transform()

The following regular layouts are supported:

  • Graph.layout_star: star layout

  • Graph.layout_circle: circular/spherical layout

  • Graph.layout_grid: regular grid layout in 2D

  • Graph.layout_grid_3d: regular grid layout in 3D

  • Graph.layout_random: random layout (2D and 3D)

The following algorithms produce nice layouts for general graphs:

  • Graph.layout_davidson_harel: Davidson-Harel layout, based on simulated annealing optimization including edge crossings

  • Graph.layout_drl: DrL layout for large graphs (2D and 3D), a scalable force-directed layout

  • Graph.layout_fruchterman_reingold: Fruchterman-Reingold layout (2D and 3D), a “spring-electric” layout based on classical physics

  • Graph.layout_graphopt: the graphopt algorithm, another force-directed layout

  • Graph.layout_kamada_kawai: Kamada-Kawai layout (2D and 3D), a “spring” layout based on classical physics

  • Graph.layout_lgl: Large Graph Layout

  • Graph.layout_mds: multidimensional scaling layout

The following algorithms are useful for trees (and for Sugiyama directed acyclic graphs or DAGs):

  • Graph.layout_reingold_tilford: Reingold-Tilford layout

  • Graph.layout_reingold_tilford_circular: circular Reingold-Tilford layout

  • Graph.layout_sugiyama: Sugiyama layout, a hierarchical layout

For bipartite graphs, there is a dedicated function:

  • Graph.layout_bipartite: bipartite layout

More might be added in the future, based on request.

Graph plotting

Once the layout of a graph has been computed, igraph can assist with the plotting itself. Plotting happens within a single function, igraph.plot.

Plotting with the default image viewer

A naked call to igraph.plot generates a temporary file and opens it with the default image viewer:

>>> ig.plot(g)

(see below if you are using this in a Jupyter notebook). This uses the Cairo library behind the scenes.

Saving a plot to a file

A call to igraph.plot with a target argument stores the graph image in the specified file and does not open it automatically. Based on the filename extension, any of the following output formats can be chosen: PNG, PDF, SVG and PostScript:

>>> ig.plot(g, target='myfile.pdf')


PNG is a raster image format while PDF, SVG, and Postscript are vector image formats. Choose one of the last three formats if you are planning on refining the image with a vector image editor such as Inkscape or Illustrator.

Plotting graphs within Matplotlib figures

If the target argument is a matplotlib axes, the graph will be plotted inside that axes:

>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots()
>>> ig.plot(g, target=ax)

You can then further manipulate the axes and figure however you like via the ax and fig variables (or whatever you called them). This variant does not use Cairo directly and might be lacking some features that are available in the Cairo backend: please open an issue on Github to request specific features.

Plotting via matplotlib makes it easy to combine igraph with other plots. For instance, if you want to have a figure with two panels showing different aspects of some data set, say a graph and a bar plot, you can easily do that:

>>> import matplotlib.pyplot as plt
>>> fig, axs = plt.subplots(1, 2, figsize=(8, 4))
>>> ig.plot(g, target=axs[0])
>>> axs[1].bar(x=[0, 1, 2], height=[1, 5, 3], color='tomato')

Another common situation is modifying the graph plot after the fact, to achieve some kind of customization. For instance, you might want to change the size and color of the vertices:

>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots()
>>> ig.plot(g, target=ax)
>>> dots = ax.get_children()[0] # This is a PathCollection
>>> dots.set_color('tomato')
>>> dots.set_sizes([250] * g.vcount())

That also helps as a workaround if you cannot figure out how to use the plotting options below: just use the defaults and then customize the appearance of your graph via standard matplotlib tools.

Plotting graphs in Jupyter notebooks

igraph supports inline plots within a Jupyter notebook via both the Cairo and matplotlib backend. If you are calling igraph.plot from a notebook cell without a matplotlib axes, the image will be shown inline in the corresponding output cell. Use the bbox argument to scale the image while preserving the size of the vertices, text, and other artists. For instance, to get a compact plot:

>>> ig.plot(g, bbox=(0, 0, 100, 100))

These inline plots can be either in PNG or SVG format. There is currently an open bug that makes SVG fail if more than one graph per notebook is plotted: we are working on a fix for that. In the meanwhile, you can use PNG representation.

If you want to use the matplotlib engine in a Jupyter notebook, you can use the recipe above. First create an axes, then tell igraph.plot about it via the target argument:

>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots()
>>> ig.plot(g, target=ax)

Exporting to other graph formats

If igraph is missing a certain plotting feature and you cannot wait for us to include it, you can always export your graph to a number of formats and use an external graph plotting tool. We support both conversion to file (e.g. DOT format used by graphviz) and to popular graph libraries such as networkx and graph-tool:

>>> dot = g.write('/myfolder/')
>>> n = g.to_networkx()
>>> gt = g.to_graph_tool()

You do not need to have any libraries installed if you export to file, but you do need them to convert directly to external Python objects (networkx, graph-tool).

Plotting options

You can add an argument layout to the plot function to specify a precomputed layout, e.g.:

>>> layout = g.layout("kamada_kawai")
>>> ig.plot(g, layout=layout)

It is also possible to use the name of the layout algorithm directly:

>>> ig.plot(g, layout="kamada_kawai")

If the layout is left unspecified, igraph uses the dedicated layout_auto() function, which chooses between one of several possible layouts based on the number of vertices and edges.

You can also specify vertex and edge color, size, and labels - and more - via additional arguments, e.g.:

>>> ig.plot(g,
...         vertex_size=20,
...         vertex_color=['blue', 'red', 'green', 'yellow'],
...         vertex_label=['first', 'second', 'third', 'fourth'],
...         edge_width=[1, 4],
...         edge_color=['black', 'grey'],
...         )

See the tutorial for examples and a full list of options.