Agent-Skills.md

Agent Skills: NetworkX Graph Analysis

Use when "NetworkX", "graph analysis", "network analysis", "graph algorithms", "shortest path", "centrality", "PageRank", "community detection", "social network", "knowledge graph"

UncategorizedID: eyadsibai/ltk/networkx

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eyadsibai/ltk
eyadsibai
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plugins/ltk-data/skills/networkx/SKILL.md

Skill Metadata

Name
networkx
Description
Use when "NetworkX", "graph analysis", "network analysis", "graph algorithms", "shortest path", "centrality", "PageRank", "community detection", "social network", "knowledge graph"

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