Drawing Directed Graph with Edge meta-data (with NetworkX in Pyt

ghz 12hours ago ⋅ 4 views

Drawing Directed Graph with Edge meta-data (with NetworkX in Python)

I have a directed multigraph that I want to represent as a (complete) directed graph with edge meta-data such that if there are e number of edges from node A to node B (in the original multigraph) then I save e as the meta-data for the edge (A,B) in the new (not-multi) directed graph.

I can construct the graph as follows:

DG = nx.complete_graph(node_list, create_using= nx.DiGraph() )

where node_list = ['node_A', 'node_B', ....]

I can add the edges using:

DG.edges[('node_A', 'node_B')]['edge_count'] = 1

But how do I print this value (nicely) using the draw command? I tried the following

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

But the edge values hide; what's more, I would need a nice way to show the meta-data associated with edge (A,B) and easily distinguishing it from edge (B,A).

Answer

To visually display the edge meta-data, such as the edge_count in a directed graph using networkx, you'll need to customize the edge labels in the plot. Here's a step-by-step guide to achieve that:

1. Constructing the Graph

You are correct that you can construct the directed graph with nx.complete_graph(). If you already have the directed multigraph and want to convert it to a directed graph with the edge meta-data (e.g., the edge count), here's how you can do it.

First, let's assume you have a multigraph G:

import networkx as nx
import matplotlib.pyplot as plt

# Example of a directed multigraph
G = nx.MultiDiGraph()

# Adding edges (with multiple edges between the same nodes)
G.add_edge('node_A', 'node_B')
G.add_edge('node_A', 'node_B')
G.add_edge('node_B', 'node_A')
G.add_edge('node_C', 'node_B')

2. Convert Multigraph to a Directed Graph with Edge Meta-Data

Next, we'll convert this multigraph to a standard directed graph (DiGraph), where each edge stores the number of occurrences (or counts) as the edge meta-data.

# Create an empty directed graph
DG = nx.DiGraph()

# Loop over each edge in the multigraph
for u, v, data in G.edges(data=True):
    # Count the number of edges between u and v
    if DG.has_edge(u, v):
        DG[u][v]['edge_count'] += 1
    else:
        DG.add_edge(u, v, edge_count=1)

3. Visualize the Graph with Edge Labels

To show the edge meta-data (i.e., the edge_count), we can use nx.draw() and pass custom labels for the edges.

Here's how you can do that:

# Draw the graph with custom edge labels (edge_count)
pos = nx.spring_layout(DG)  # Position for nodes

# Draw the nodes and the edges
nx.draw(DG, pos, with_labels=True, node_size=2000, node_color='skyblue', font_size=10, font_weight='bold')

# Create a dictionary with edge labels (edge_count)
edge_labels = nx.get_edge_attributes(DG, 'edge_count')

# Draw the edge labels on the graph
nx.draw_networkx_edge_labels(DG, pos, edge_labels=edge_labels)

# Show the plot
plt.show()

4. Customizing the Appearance

  • pos = nx.spring_layout(DG) computes the positions of nodes. You can experiment with other layouts such as circular_layout, kamada_kawai_layout, or spectral_layout depending on how you want your graph to appear.
  • node_size=2000, node_color='skyblue', font_size=10, and font_weight='bold' are all customizations for the node appearance. You can adjust these as you like.
  • nx.draw_networkx_edge_labels() will display the edge labels, which in this case will show the edge_count for each edge.

5. Displaying Edge Meta-Data Nicely

This method should allow you to display the number of edges between nodes (the edge_count) as edge labels on your graph. Each edge between node A and node B will show the number of times that edge appears in the original multigraph.

Example Output

After running the above code, your plot should display a directed graph with edge labels indicating the count of edges between each pair of nodes. For example:

  • If there are two edges from node_A to node_B, the edge between them will have a label "2".
  • If there is one edge from node_B to node_A, it will have the label "1".

This should help you visualize the structure of your graph, including edge counts, clearly.

Notes:

  1. Edge Direction: Since you’re working with a directed graph, ensure that the edges are directed, and labels are placed appropriately.
  2. Overlapping Labels: If the edge labels overlap, you may need to adjust the positions slightly or customize the layout to better fit the graph.

By using the nx.get_edge_attributes() function and nx.draw_networkx_edge_labels(), you can efficiently display edge meta-data like edge_count directly on the graph.

#py