Social Network Community Detection

import matplotlib.pyplot as plt
import numpy as np

np.random.seed(123)

# Social network node positions
n_nodes = 150

# Create community structure
communities = 4
colors_community = ['#27D3F5', '#F5276C', '#6CF527', '#F5B027']

all_x, all_y, all_z, all_c, all_s = [], [], [], [], []

for i in range(communities):
    n = n_nodes // communities
    angle = i * 2 * np.pi / communities
    cx, cy = 3 * np.cos(angle), 3 * np.sin(angle)
    
    x = np.random.normal(cx, 0.8, n)
    y = np.random.normal(cy, 0.8, n)
    z = np.random.normal(0, 0.5, n)
    
    # Node importance (degree)
    importance = np.random.exponential(30, n) + 20
    
    all_x.extend(x)
    all_y.extend(y)
    all_z.extend(z)
    all_c.extend([colors_community[i]] * n)
    all_s.extend(importance)

fig = plt.figure(figsize=(10, 8), facecolor='#ffffff')
ax = fig.add_subplot(111, projection='3d', facecolor='#ffffff')

ax.scatter(all_x, all_y, all_z, c=all_c, s=all_s, alpha=0.7, 
           edgecolors='#374151', linewidths=0.3)

ax.set_xlabel('Dimension 1', color='#1f2937', fontsize=10)
ax.set_ylabel('Dimension 2', color='#1f2937', fontsize=10)
ax.set_zlabel('Dimension 3', color='#1f2937', fontsize=10)
ax.set_title('Social Network Community Detection', color='#1f2937', fontsize=14, fontweight='bold', pad=20)

ax.tick_params(colors='#6b7280', labelsize=8)
ax.xaxis.pane.fill = False
ax.yaxis.pane.fill = False
ax.zaxis.pane.fill = False
ax.xaxis.pane.set_edgecolor('#e5e7eb')
ax.yaxis.pane.set_edgecolor('#e5e7eb')
ax.zaxis.pane.set_edgecolor('#e5e7eb')

ax.view_init(elev=25, azim=45)
plt.tight_layout()
plt.show()