| name | networkx |
| description | Use when "NetworkX", "graph analysis", "network analysis", "graph algorithms", "shortest path", "centrality", "PageRank", "community detection", "social network", "knowledge graph" |
| version | 1.0.0 |
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)
Graph Types
| Type | Edges | Multiple Edges |
|---|
Graph | Undirected | No |
DiGraph | Directed | No |
MultiGraph | Undirected | Yes |
MultiDiGraph | Directed | Yes |
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 |
Graph Generators
| Generator | Model |
|---|
| Erdős-Rényi | Random edges |
| Barabási-Albert | Preferential attachment (scale-free) |
| Watts-Strogatz | Small-world |
| Complete | All connected |
Layout Algorithms
| Layout | Best For |
|---|
| Spring | General purpose |
| Circular | Regular structure |
| Kamada-Kawai | Aesthetics |
| Spectral | Clustered graphs |
I/O Formats
| Format | Preserves Attributes | Human Readable |
|---|
| GraphML | Yes | Yes (XML) |
| Edge list | No | Yes |
| JSON | Yes | Yes |
| Pandas | Yes | Via DataFrame |
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.
Best Practices
- Set random seeds for reproducibility
- Choose correct graph type upfront
- Use pandas integration for data exchange
- Consider memory for large graphs
Resources
