NetworkX

When to Use This Skill

Invoke this skill when tasks involve:

• 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

1. Graph Creation and Manipulation

NetworkX supports four main graph types:

• Graph: Undirected graphs with single edges
• DiGraph: Directed graphs with one-way connections
• MultiGraph: Undirected graphs allowing multiple edges between nodes
• MultiDiGraph: Directed graphs with multiple edges

Create graphs by:

import networkx as nx

## Add edges

G.add_edge(1, 2)
G.add_edges_from([(1, 3), (2, 4)])
G.add_edge(1, 4, weight=0.8, relation='interacts')

Reference: See references/graph-basics.md for comprehensive guidance on creating, modifying, examining, and managing graph structures, including working with attributes and subgraphs.