networkx

combined

Creates, analyzes, and visualizes complex networks and graphs in Python with NetworkX. Use when working with network/graph data structures, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks (random, scale-free, small-world), reading/writing graph file formats, or drawing network topologies. Common applications include social, biological, transportation, and citation networks. Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.

When to use

**Creating graphs**: Building network structures from data, adding nodes and edges with attributes
**Graph analysis**: Computing centrality measures, finding shortest paths, detecting communities, measuring clustering
**Graph algorithms**: Running standard algorithms like Dijkstra's, PageRank, minimum spanning trees, maximum flow
**Network generation**: Creating synthetic networks (random, scale-free, small-world models) for testing or simulation
**Graph I/O**: Reading from or writing to various formats (edge lists, GraphML, JSON, CSV, adjacency matrices)
**Visualization**: Drawing and customizing network visualizations with matplotlib or interactive libraries
**Network comparison**: Checking isomorphism, computing graph metrics, analyzing structural properties

When not to use

Use this skill only when the task clearly matches the scope described above.
Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

Skill page

Conflict

The K-Dense-AI copy is the upstream source (v1.4, NetworkX 3.7) and contains material constraints and API corrections that the other copies lack or contradict. Specifically, it warns that `nx.random_tree` was removed in NetworkX 3.4 and provides the replacement `nx.random_labeled_tree`, whereas the other three copies still use the deprecated/removed `nx.random_tree`. It also adds specific guidance on weight semantics for shortest paths and betweenness centrality (requiring strictly positive distances and explicit conversion for similarity metrics) and specifies `weight=None` for unweighted community detection. The other copies are reproductions of the older version of this skill, so crowning one of them would hide the critical version-specific API changes and safety constraints present in the K-Dense-AI copy.