Python Advanced - Graph Analytics with NetworkX

Analyzing and visualizing graphs using NetworkX in Python

NetworkX is a powerful Python library for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. This tutorial explores how to use NetworkX for analyzing and visualizing graphs in Python.

Installing NetworkX

To use NetworkX, you need to install it using pip:

pip install networkx
            

Creating a Graph

Here is an example of creating a simple graph with NetworkX:

import networkx as nx
import matplotlib.pyplot as plt

# Create a new graph
G = nx.Graph()

# Add nodes
G.add_node(1)
G.add_nodes_from([2, 3, 4])

# Add edges
G.add_edge(1, 2)
G.add_edges_from([(2, 3), (3, 4)])

# Draw the graph
nx.draw(G, with_labels=True)
plt.show()
            

Analyzing Graph Properties

NetworkX provides functions to analyze various properties of graphs. Here is an example:

# Calculate the number of nodes and edges
num_nodes = G.number_of_nodes()
num_edges = G.number_of_edges()
print(f"Number of nodes: {num_nodes}")
print(f"Number of edges: {num_edges}")

# Calculate the degree of each node
degrees = dict(G.degree())
print(f"Degrees: {degrees}")

# Calculate the shortest path between two nodes
shortest_path = nx.shortest_path(G, source=1, target=4)
print(f"Shortest path from 1 to 4: {shortest_path}")
            

Visualizing Graphs

NetworkX integrates with Matplotlib to provide easy visualization of graphs. Here is an example of visualizing a graph with different layouts:

# Draw the graph with a circular layout
plt.figure(figsize=(8, 6))
nx.draw_circular(G, with_labels=True)
plt.title('Circular Layout')
plt.show()

# Draw the graph with a spring layout
plt.figure(figsize=(8, 6))
nx.draw_spring(G, with_labels=True)
plt.title('Spring Layout')
plt.show()
            

Directed Graphs

NetworkX supports directed graphs. Here is an example:

# Create a directed graph
DG = nx.DiGraph()

# Add nodes and edges
DG.add_edges_from([(1, 2), (2, 3), (3, 4), (4, 1)])

# Draw the directed graph
plt.figure(figsize=(8, 6))
nx.draw(DG, with_labels=True, arrows=True)
plt.title('Directed Graph')
plt.show()
            

Weighted Graphs

NetworkX supports weighted graphs. Here is an example:

# Create a weighted graph
WG = nx.Graph()

# Add nodes and weighted edges
WG.add_weighted_edges_from([(1, 2, 4.2), (2, 3, 3.1), (3, 4, 5.7)])

# Draw the weighted graph
pos = nx.spring_layout(WG)
edge_labels = nx.get_edge_attributes(WG, 'weight')
plt.figure(figsize=(8, 6))
nx.draw(WG, pos, with_labels=True)
nx.draw_networkx_edge_labels(WG, pos, edge_labels=edge_labels)
plt.title('Weighted Graph')
plt.show()
            

Graph Algorithms

NetworkX provides various graph algorithms. Here is an example of using the PageRank algorithm:

# Calculate PageRank
pagerank = nx.pagerank(G)
print(f"PageRank: {pagerank}")
            

Community Detection

Here is an example of detecting communities in a graph using the Girvan-Newman algorithm:

from networkx.algorithms.community import girvan_newman

# Detect communities
communities = girvan_newman(G)
first_level_communities = next(communities)
community_list = [list(community) for community in first_level_communities]
print(f"Communities: {community_list}")
            

Summary

In this tutorial, you learned about analyzing and visualizing graphs using NetworkX in Python. NetworkX provides a comprehensive framework for the creation, manipulation, and study of complex networks. Understanding how to create graphs, analyze graph properties, visualize graphs, and use graph algorithms can help you leverage NetworkX for various graph analytics applications.