miserables_network.py

import networkx as nx
import matplotlib.pyplot as plt
from networkx.algorithms.community import greedy_modularity_communities
import matplotlib.cm as cm
import numpy as np

# Load graph
G = nx.les_miserables_graph()

# Compute centrality
degree_centrality = nx.degree_centrality(G)

# Detect communities
communities = list(greedy_modularity_communities(G))
node_community_map = {}
for i, comm in enumerate(communities):
    for name in comm:
        node_community_map[name] = i

# Prepare layout
pos = nx.spring_layout(G, seed=42)

# Node color by community
cmap = cm.get_cmap('Set3', len(communities))
node_colors = [cmap(node_community_map[node]) for node in G.nodes()]

# Node size by centrality
node_sizes = [degree_centrality[node] * 3000 for node in G.nodes()]

# Edge width by weight
edge_weights = [G[u][v]['weight'] for u, v in G.edges()]
edge_widths = [w / 2 for w in edge_weights]

# Draw nodes
plt.figure(figsize=(14, 10))
nx.draw_networkx_nodes(G, pos,
                       node_size=node_sizes,
                       node_color=node_colors,
                       alpha=0.9)

# Draw edges
nx.draw_networkx_edges(G, pos,
                       width=edge_widths,
                       alpha=0.3)

# Draw labels for top central nodes only (optional)
top_nodes = sorted(degree_centrality.items(), key=lambda x: x[1], reverse=True)[:10]
labels = {name: name for name, _ in top_nodes}
nx.draw_networkx_labels(G, pos, labels, font_size=9, font_color='black')

# Title and background
plt.title('Les Misérables Character Network (Centrality & Community)', fontsize=16)
plt.axis('off')
plt.gca().set_facecolor('none')
plt.gcf().patch.set_alpha(0.0)
plt.tight_layout()
plt.show()