Networkx
- 61 installs
- 7 repo stars
- Updated January 15, 2026
- eyadsibai/ltk
Helps with ai & agent building tasks.
About
networkx is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted development.
- networkx
- AI & Agent Building
- AI-coding skill
Networkx by the numbers
- 61 all-time installs (skills.sh)
- Ranked #6,381 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
- Data as of Jul 30, 2026 (Skillselion catalog sync)
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| Installs | 61 |
|---|---|
| repo stars | ★ 7 |
| Last updated | January 15, 2026 |
| Repository | eyadsibai/ltk ↗ |
What it does
Helps with ai & agent building tasks.
Files
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
- NetworkX docs: <https://networkx.org/documentation/latest/>
- Tutorial: <https://networkx.org/documentation/latest/tutorial.html>
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