networkx

NetworkX - Network Analysis and Graph Theory

NetworkX is the go-to library for analyzing complex networks. It treats graphs as flexible containers for nodes (any hashable object) and edges, which can carry arbitrary metadata.

When to Use

• Analyzing social, biological, or infrastructure networks.
• Calculating path metrics (shortest paths, diameters, flow).
• Measuring node importance (Centrality, PageRank).
• Detecting communities and clusters within a network.
• Generating random graph models (Erdős-Rényi, Barabási-Albert).
• Finding connectivity components and cliques.
• Designing and optimizing routing or dependency trees.

Reference Documentation

Official docs: https://networkx.org/
Algorithm reference: https://networkx.org/documentation/stable/reference/algorithms/index.html
Search patterns: nx.Graph, nx.shortest_path, nx.degree_centrality, nx.connected_components

Core Principles

Graph Types

Class	Description
Graph	Undirected graph; ignores self-loops if added twice.
DiGraph	Directed graph; edges have a specific direction (A → B ≠ B → A).
MultiGraph	Undirected; allows multiple edges between the same two nodes.
MultiDiGraph	Directed; multiple directed edges between nodes.

Nodes and Edges

• Nodes: Can be any hashable Python object (strings, numbers, tuples, even objects).
• Edges: Represent a relationship between two nodes. Can store attributes like weight, capacity, or label.

Quick Reference

Installation

pip install networkx matplotlib scipy

Standard Imports

import networkx as nx
import matplotlib.pyplot as plt
import numpy as np

Basic Pattern - Creation and Analysis

import networkx as nx

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

# 2. Add edges (nodes are created automatically)
G.add_edge("A", "B", weight=4.5)
G.add_edges_from([("B", "C"), ("C", "A"), ("C", "D")])

# 3. Analyze
print(f"Nodes: {G.number_of_nodes()}")
print(f"Shortest path A to D: {nx.shortest_path(G, 'A', 'D')}")

# 4. Draw
nx.draw(G, with_labels=True)