Communities ¶
Communities¶
This example shows how to visualize communities or clusters of a graph. First, make the graph: we just use a famous graph here for simplicity.
import igraph as ig
import matplotlib.pyplot as plt
g = ig.Graph.Famous("Zachary")
Second, define the clusters.
# Use edge betweenness to detect communities
communities = g.community_edge_betweenness()
# ... and convert into a VertexClustering for plotting
communities = communities.as_clustering()
Third, prepare colors for the various communities:
# Color each vertex and edge based on its community membership
num_communities = len(communities)
palette = ig.RainbowPalette(n=num_communities)
for i, community in enumerate(communities):
g.vs[community]["color"] = i
community_edges = g.es.select(_within=community)
community_edges["color"] = i
Finally, plot the graph:
# Plot with only vertex and edge coloring
fig, ax = plt.subplots()
ig.plot(
communities,
palette=palette,
edge_width=1,
target=ax,
vertex_size=0.3,
)
… and add a fancy legend via proxy artists:
legend_handles = []
for i in range(num_communities):
handle = ax.scatter(
[], [],
s=100,
facecolor=palette.get(i),
edgecolor="k",
label=i,
)
legend_handles.append(handle)
ax.legend(
handles=legend_handles,
title='Community:',
bbox_to_anchor=(0, 1.0),
bbox_transform=ax.transAxes,
)
The resulting figure is shown below.
The graph with its communities highlighted by color.¶
For an example on how to generate the cluster graph from a vertex cluster, check out Generating Cluster Graphs.