NetworkX Graph Analysis

Python library for creating, analyzing, and visualizing networks and graphs.

When to Use

• Social network analysis
• Knowledge graphs and ontologies
• Shortest path problems
• Community detection
• Citation/reference networks
• Biological networks (protein interactions)

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Graph Types

Type	Edges	Multiple Edges
Graph	Undirected	No
DiGraph	Directed	No
MultiGraph	Undirected	Yes
MultiDiGraph	Directed	Yes

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Key Algorithms

Centrality Measures

Measure	What It Finds	Use Case
Degree	Most connections	Popular nodes
Betweenness	Bridge nodes	Information flow
Closeness	Fastest reach	Efficient spreaders
PageRank	Importance	Web pages, citations
Eigenvector	Influential connections	Who knows important people

Path Algorithms

Algorithm	Purpose
Shortest path	Minimum hops
Weighted shortest	Minimum cost
All pairs shortest	Full distance matrix
Dijkstra	Efficient weighted paths

Community Detection

Method	Approach
Louvain	Modularity optimization
Greedy modularity	Hierarchical merging
Label propagation	Fast, scalable

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Graph Generators

Generator	Model
Erdős-Rényi	Random edges
Barabási-Albert	Preferential attachment (scale-free)
Watts-Strogatz	Small-world
Complete	All connected

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Layout Algorithms

Layout	Best For
Spring	General purpose
Circular	Regular structure
Kamada-Kawai	Aesthetics
Spectral	Clustered graphs

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I/O Formats

Format	Preserves Attributes	Human Readable
GraphML	Yes	Yes (XML)
Edge list	No	Yes
JSON	Yes	Yes
Pandas	Yes	Via DataFrame

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Performance Considerations

Scale	Approach
< 10K nodes	Any algorithm
10K - 100K	Use approximate algorithms
> 100K	Consider graph-tool or igraph

Key concept: NetworkX is pure Python - great for prototyping, may need alternatives for production scale.

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Best Practices

• Set random seeds for reproducibility
• Choose correct graph type upfront
• Use pandas integration for data exchange
• Consider memory for large graphs

Resources

• NetworkX docs: https://networkx.org/documentation/latest/
• Tutorial: https://networkx.org/documentation/latest/tutorial.html