171

I have some nodes coming from a script that I want to map on to a graph. In the below, I want to use Arrow to go from A to D and probably have the edge colored too in (red or something).

This is basically, like a path from A to D when all other nodes are present. you can imagine each nodes as cities and traveling from A to D requires directions (with arrow heads).

This code below builds the graph

import networkx as nx
import numpy as np
import matplotlib.pyplot as plt

G = nx.Graph()
G.add_edges_from(
    [('A', 'B'), ('A', 'C'), ('D', 'B'), ('E', 'C'), ('E', 'F'),
     ('B', 'H'), ('B', 'G'), ('B', 'F'), ('C', 'G')])

val_map = {'A': 1.0,
           'D': 0.5714285714285714,
           'H': 0.0}

values = [val_map.get(node, 0.25) for node in G.nodes()]

nx.draw(G, cmap = plt.get_cmap('jet'), node_color = values)
plt.show()

but I want something like shown in the image.enter image description here enter image description here

Arrow heads of the first image and the edges in red color onto the second image.

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7 Answers 7

Reset to default
141

Fully fleshed out example with arrows for only the red edges:

import networkx as nx
import matplotlib.pyplot as plt

G = nx.DiGraph()
G.add_edges_from(
    [('A', 'B'), ('A', 'C'), ('D', 'B'), ('E', 'C'), ('E', 'F'),
     ('B', 'H'), ('B', 'G'), ('B', 'F'), ('C', 'G')])

val_map = {'A': 1.0,
           'D': 0.5714285714285714,
           'H': 0.0}

values = [val_map.get(node, 0.25) for node in G.nodes()]

# Specify the edges you want here
red_edges = [('A', 'C'), ('E', 'C')]
edge_colours = ['black' if not edge in red_edges else 'red'
                for edge in G.edges()]
black_edges = [edge for edge in G.edges() if edge not in red_edges]

# Need to create a layout when doing
# separate calls to draw nodes and edges
pos = nx.spring_layout(G)
nx.draw_networkx_nodes(G, pos, cmap=plt.get_cmap('jet'), 
                       node_color = values, node_size = 500)
nx.draw_networkx_labels(G, pos)
nx.draw_networkx_edges(G, pos, edgelist=red_edges, edge_color='r', arrows=True)
nx.draw_networkx_edges(G, pos, edgelist=black_edges, arrows=False)
plt.show()

Red edges

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10 Comments

it's crazy how different our two updated images are. +1 for figuring out the edge colors!
why is your edge (C,E) not red, though it must be red according to your code above?
is it not possible to have these arrow heads only on edges of interest? for example (A,C) and (C,E)
@user1988876: Ah, sorry, (C, E) is not red because I picked the edges for red_edges when I was still working with your undirected graph, just picking randomly from the edges returned by G.edges(). It should be red_edges = [('A', 'C'), ('E', 'C')].
@user1988876: Having arrows on only some of the edges is possible with separate calls to draw_networkx_edges(). I've cleaned up the code and fixed the DiGraph issues.
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68

Instead of regular nx.draw you may want to use:

nx.draw_networkx(G[, pos, arrows, with_labels])

For example:

nx.draw_networkx(G, arrows=True, **options)

You can add options by initialising that ** variable like this:

options = {
    'node_color': 'blue',
    'node_size': 100,
    'width': 3,
    'arrowstyle': '-|>',
    'arrowsize': 12,
}

Also some functions support the directed=True parameter In this case this state is the default one:

G = nx.DiGraph(directed=True)

The networkx reference is found here.

Graph with arrows image

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1 Comment

It would be great to give a working example
58

I only put this in for completeness. I've learned plenty from marius and mdml. Here are the edge weights. Sorry about the arrows. Looks like I'm not the only one saying it can't be helped. I couldn't render this with ipython notebook I had to go straight from python which was the problem with getting my edge weights in sooner. 

import networkx as nx
import numpy as np
import matplotlib.pyplot as plt
import pylab

G = nx.DiGraph()

G.add_edges_from([('A', 'B'),('C','D'),('G','D')], weight=1)
G.add_edges_from([('D','A'),('D','E'),('B','D'),('D','E')], weight=2)
G.add_edges_from([('B','C'),('E','F')], weight=3)
G.add_edges_from([('C','F')], weight=4)


val_map = {'A': 1.0,
                   'D': 0.5714285714285714,
                              'H': 0.0}

values = [val_map.get(node, 0.45) for node in G.nodes()]
edge_labels=dict([((u,v,),d['weight'])
                 for u,v,d in G.edges(data=True)])
red_edges = [('C','D'),('D','A')]
edge_colors = ['black' if not edge in red_edges else 'red' for edge in G.edges()]

pos=nx.spring_layout(G)
nx.draw_networkx_edge_labels(G,pos,edge_labels=edge_labels)
nx.draw(G,pos, node_color = values, node_size=1500,edge_color=edge_colors,edge_cmap=plt.cm.Reds)
pylab.show()

enter image description here

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8 Comments

I ran this and didn't get the node labels. You need to add these: node_labels = {node:node for node in G.nodes()}; nx.draw_networkx_labels(G, pos, labels=node_labels) .
And what if you already have an undirected graph and want to reproduce a directed copy of it? Is there any way of setting the G.add_edges_from() line without having to manually enter the start and endpoint? Perhaps adding edges from a dict?
Is it possible to get real arrows on the edges? I don't like the just thicker end.
Drawing arrowheads in matplotlib is tricky and currently not supported in NetworkX. Pull requests are accepted.
Update: NetworkX Arrows have been implemented. Huzzah!
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30

You need to use a directed graph instead of a graph, i.e.

G = nx.DiGraph()

Then, create a list of the edge colors you want to use and pass those to nx.draw (as shown by @Marius).

Putting this all together, I get the image below. Still not quite the other picture you show (I don't know where your edge weights are coming from), but much closer! If you want more control of how your output graph looks (e.g. get arrowheads that look like arrows), I'd check out NetworkX with Graphviz.

enter image description here

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2 Comments

Ah cheers, I couldn't figure out why arrows weren't working as I could see arguments for them in the documentation.
I tried what you did bu did g = nx.Graph(strict=True, directed=True). The plots are not directed. But it was fixed with g = g.to_directed(). Am I doing something wrong or is this a bug?
19 
import networkx as nx
import matplotlib.pyplot as plt

g = nx.DiGraph()
g.add_nodes_from([1,2,3,4,5])
g.add_edge(1,2)
g.add_edge(4,2)
g.add_edge(3,5)
g.add_edge(2,3)
g.add_edge(5,4)

nx.draw(g,with_labels=True)
plt.draw()
plt.show()

This is just simple how to draw directed graph using python 3.x using networkx. just simple representation and can be modified and colored etc. See the generated graph here.

Note: It's just a simple representation. Weighted Edges could be added like

g.add_edges_from([(1,2),(2,5)], weight=2)

and hence plotted again.

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1 Comment

Simplest answer wins the medal 🥇
16 
import networkx as nx
import matplotlib.pyplot as plt

G = nx.DiGraph()
G.add_node("A")
G.add_node("B")
G.add_node("C")
G.add_node("D")
G.add_node("E")
G.add_node("F")
G.add_node("G")
G.add_edge("A","B")
G.add_edge("B","C")
G.add_edge("C","E")
G.add_edge("C","F")
G.add_edge("D","E")
G.add_edge("F","G")

print(G.nodes())
print(G.edges())

pos = nx.spring_layout(G)

nx.draw_networkx_nodes(G, pos)
nx.draw_networkx_labels(G, pos)
nx.draw_networkx_edges(G, pos, edge_color='r', arrows = True)

plt.show()
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1 Comment

I tried what you did bu did g = nx.Graph(strict=True, directed=True). The plots are not directed. But it was fixed with g = g.to_directed(). Am I doing something wrong or is this a bug?
0

Since you've mentioned "I want something like shown in the image", I've reproduced the graph and image in Python by 1. creating the graph with NetworkX and 2. plotting it with gravis.

Disclaimer: I'm the author of gravis and developed the package for use cases like this one where you want to easily visualize a graph with labels and colors on nodes and/or edges. The graph object can come from NetworkX but also from other popular packages in Python like iGraph, graph-tool, etc.

import networkx as nx
import gravis as gv

edges = [
    ('A', 'B', 1, 'black'),
    ('B', 'C', 3, 'black'),
    ('B', 'D', 2, 'black'),
    ('B', 'E', 1, 'black'),
    ('C', 'D', 1, 'red'),
    ('C', 'E', 4, 'black'),
    ('D', 'A', 2, 'red'),
    ('D', 'E', 2, 'black'),
    ('E', 'F', 3, 'black'),
    ('G', 'D', 1, 'black'),
]
g = nx.DiGraph()
for source, target, strength, color in edges:
    g.add_edge(source, target, strength=strength, color=color)

fig = gv.d3(g, show_edge_label=True, edge_label_data_source='strength')
fig.display()  # opens the plot in a browser window, can be stored as SVG/JPG/PNG

Here's how it looks in a Jupyter notebook where the visualization is directly embedded in the result cell. You can drag nodes around, assign them to fixed positions, add edge curvature, export static images, etc. with a menu that's shown when clicking the arrow in the right upper corner. Notebook demo

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2 Comments

Is this easy to embed in a MkDocs-generated documentation site ?
Never mind, found it. Awesome tool. (Pity there is not recent update for gravis.)

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