networkx

NetworkX Skill — Create and Manipulate Networks

NetworkX (v3.6+) is the standard Python library for graph analysis. This skill covers everything from basic graph creation to advanced algorithms. When in doubt, prefer simple explicit code over clever one-liners — graphs are complex enough on their own.

References:

• algorithms.md — Algorithm reference by category (centrality, community, flow, etc.)
• io.md — File I/O and format conversion reference

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1. Choosing a Graph Class

Pick the right class first — it cannot easily be changed after construction.

import networkx as nx

G  = nx.Graph()          # undirected, no parallel edges
DG = nx.DiGraph()        # directed, no parallel edges
MG = nx.MultiGraph()     # undirected + parallel edges allowed
MD = nx.MultiDiGraph()   # directed + parallel edges allowed

Need	Class
Social networks, protein interactions	Graph
Web graphs, citation networks, DAGs	DiGraph
Transport networks (multiple routes)	MultiGraph
Dependency graphs with typed edges	MultiDiGraph

Convert between types:

DG = G.to_directed()    # Graph → DiGraph (each edge becomes two arcs)
G2 = DG.to_undirected() # DiGraph → Graph

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2. Building Graphs

Add Nodes

Any hashable Python object is a valid node: int, str, tuple, frozenset.

G.add_node(1)
G.add_node("Alice", age=30, role="engineer")   # node with attributes
G.add_nodes_from([2, 3, 4])
G.add_nodes_from([
    ("Bob",   {"age": 25, "role": "designer"}),
    ("Carol", {"age": 35, "role": "manager"}),
])

Add Edges

G.add_edge(1, 2)
G.add_edge("Alice", "Bob", weight=0.9, relation="colleague")
G.add_edges_from([(1, 2), (2, 3), (3, 4)])
G.add_edges_from([
    (1, 2, {"weight": 1.5}),
    (2, 3, {"weight": 0.8}),
])
G.add_weighted_edges_from([(1, 2, 1.5), (2, 3, 0.8)])  # shorthand

For MultiGraph, add_edge returns the edge key (int):

k = MG.add_edge(1, 2, weight=0.5)   # k=0
k = MG.add_edge(1, 2, weight=0.75)  # k=1 (parallel edge)