NetworkX

Use this skill for graph analysis that benefits from flexible Python-native data structures and readable algorithms.

Common CS Uses

• citation or collaboration graph analysis
• package dependency and call-graph analysis
• knowledge graph or heterogeneous relation prototyping
• graph features for downstream ML pipelines
• path analysis in workflows, state machines, or tool graphs
• synthetic graph generation for experiments or teaching

Workflow

1. Define what nodes and edges represent.
2. Attach attributes only when they affect the analysis.
3. Choose the graph type: directed, undirected, multigraph, weighted, or heterogeneous approximation.
4. Run the algorithms that answer the actual research question.
5. Export or visualize only after the graph semantics are clear.

Local References

Read only what is needed:

• references/graph-basics.md
• references/algorithms.md
• references/io.md
• references/generators.md
• references/visualization.md

Practical Guidance

• Use NetworkX for clarity and experimentation, not maximum scale.
• For very large graphs, use it for prototyping, sampling, or analysis on reduced representations.
• Be explicit about directedness, edge weights, and disconnected components.
• Do not assume a graph metric is meaningful unless it matches the graph semantics.

Default Output

When the user asks for graph help without a fixed format, return:

1. the graph model being assumed
2. the algorithms or metrics to compute
3. interpretation notes and likely pitfalls