Source code for networkx.algorithms.components.connected

"""Connected components."""
import networkx as nx
from networkx.utils.decorators import not_implemented_for

from ...utils import arbitrary_element

__all__ = [
    "number_connected_components",
    "connected_components",
    "is_connected",
    "node_connected_component",
]


[docs]@nx._dispatch @not_implemented_for("directed") def connected_components(G): """Generate connected components. Parameters ---------- G : NetworkX graph An undirected graph Returns ------- comp : generator of sets A generator of sets of nodes, one for each component of G. Raises ------ NetworkXNotImplemented If G is directed. Examples -------- Generate a sorted list of connected components, largest first. >>> G = nx.path_graph(4) >>> nx.add_path(G, [10, 11, 12]) >>> [len(c) for c in sorted(nx.connected_components(G), key=len, reverse=True)] [4, 3] If you only want the largest connected component, it's more efficient to use max instead of sort. >>> largest_cc = max(nx.connected_components(G), key=len) To create the induced subgraph of each component use: >>> S = [G.subgraph(c).copy() for c in nx.connected_components(G)] See Also -------- strongly_connected_components weakly_connected_components Notes ----- For undirected graphs only. """ seen = set() for v in G: if v not in seen: c = _plain_bfs(G, v) seen.update(c) yield c
[docs]def number_connected_components(G): """Returns the number of connected components. Parameters ---------- G : NetworkX graph An undirected graph. Returns ------- n : integer Number of connected components Examples -------- >>> G = nx.Graph([(0, 1), (1, 2), (5, 6), (3, 4)]) >>> nx.number_connected_components(G) 3 See Also -------- connected_components number_weakly_connected_components number_strongly_connected_components Notes ----- For undirected graphs only. """ return sum(1 for cc in connected_components(G))
[docs]@not_implemented_for("directed") def is_connected(G): """Returns True if the graph is connected, False otherwise. Parameters ---------- G : NetworkX Graph An undirected graph. Returns ------- connected : bool True if the graph is connected, false otherwise. Raises ------ NetworkXNotImplemented If G is directed. Examples -------- >>> G = nx.path_graph(4) >>> print(nx.is_connected(G)) True See Also -------- is_strongly_connected is_weakly_connected is_semiconnected is_biconnected connected_components Notes ----- For undirected graphs only. """ if len(G) == 0: raise nx.NetworkXPointlessConcept( "Connectivity is undefined ", "for the null graph." ) return sum(1 for node in _plain_bfs(G, arbitrary_element(G))) == len(G)
[docs]@not_implemented_for("directed") def node_connected_component(G, n): """Returns the set of nodes in the component of graph containing node n. Parameters ---------- G : NetworkX Graph An undirected graph. n : node label A node in G Returns ------- comp : set A set of nodes in the component of G containing node n. Raises ------ NetworkXNotImplemented If G is directed. Examples -------- >>> G = nx.Graph([(0, 1), (1, 2), (5, 6), (3, 4)]) >>> nx.node_connected_component(G, 0) # nodes of component that contains node 0 {0, 1, 2} See Also -------- connected_components Notes ----- For undirected graphs only. """ return _plain_bfs(G, n)
def _plain_bfs(G, source): """A fast BFS node generator""" G_adj = G.adj seen = set() nextlevel = {source} while nextlevel: thislevel = nextlevel nextlevel = set() for v in thislevel: if v not in seen: seen.add(v) nextlevel.update(G_adj[v]) return seen