How to draw edge weights using a weighted adjacency matrix?

Question

I have the problem that I have a weighted adjacency matrix C of a directed graph, so C(j,i)=0, whenever there is no edge from j to i and if C(j,i)>0, then C(j,i) is the weight of the edge;

Now I want to plot the Directed Graph. There are many solutions when you manually add edges, see e.g. here:

Add edge-weights to plot output in networkx

But I want to plot edges and edge weights based on my matrix C; I started the following way:

def DrawGraph(C):

    import networkx as nx
    import matplotlib.pyplot as plt 


    G = nx.DiGraph(C)

    plt.figure(figsize=(8,8))
    nx.draw(G, with_labels=True)

This plots a graph and there are labels on the vertices, but there are no edge weights - also I cannot adapt the technic from the upper link to make it work - so what could I do?

And how would I change node size and color?

CDJB CDJB · Accepted Answer · 2019-12-11T09:54:21

There are various ways to do this using networkx - here is a solution which should fit your requirements:

Code:

# Set up weighted adjacency matrix
A = np.array([[0, 0, 0],
              [2, 0, 3],
              [5, 0, 0]])

# Create DiGraph from A
G = nx.from_numpy_matrix(A, create_using=nx.DiGraph)

# Use spring_layout to handle positioning of graph
layout = nx.spring_layout(G)

# Use a list for node_sizes
sizes = [1000,400,200]

# Use a list for node colours
color_map = ['g', 'b', 'r']

# Draw the graph using the layout - with_labels=True if you want node labels.
nx.draw(G, layout, with_labels=True, node_size=sizes, node_color=color_map)

# Get weights of each edge and assign to labels
labels = nx.get_edge_attributes(G, "weight")

# Draw edge labels using layout and list of labels
nx.draw_networkx_edge_labels(G, pos=layout, edge_labels=labels)

# Show plot
plt.show()

Result:

How to draw edge weights using a weighted adjacency matrix?

1 Answers