NetworkX Graph
Visualize nodes and edges via Matplotlib.
How it Works
NetworkX algorithmic maps push interconnected nodes down to coordinates arrays.
The mapping functions link correctly into matplotlib endpoints inside browser memory.
Source Code
Generates a random connected graph tree plotted natively.
graphs.py
Try in Editorimport networkx as nx
import matplotlib.pyplot as plt
# Create a graph
G = nx.erdos_renyi_graph(n=12, p=0.3, seed=42)
# Calculate centrality
centrality = nx.degree_centrality(G)
node_sizes = [v * 3000 for v in centrality.values()]
node_colors = list(centrality.values())
fig, ax = plt.subplots(figsize=(8, 6))
pos = nx.spring_layout(G, seed=42)
# Draw graph
nodes = nx.draw_networkx_nodes(
G, pos,
node_size=node_sizes,
node_color=node_colors,
cmap=plt.cm.viridis,
edgecolors='white',
linewidths=2
)
nx.draw_networkx_edges(G, pos, edge_color='gray', alpha=0.5, width=1.5)
nx.draw_networkx_labels(G, pos, font_color='white', font_family='sans-serif', font_weight='bold')
plt.title("Network Connectedness", fontsize=14, pad=15)
plt.axis('off')
plt.show()Terminal Output
Rendered Network Graph in the Graphs tab...Real-world Applications
- Algorithmic theory
- Social node rendering
- Topological queries