weakly_connected_components(G)
For directed graphs only.
A directed graph
If G is undirected.
A generator of sets of nodes, one for each weakly connected component of G.
Generate weakly connected components of G.
Generate a sorted list of weakly connected components, largest first.
>>> G = nx.path_graph(4, create_using=nx.DiGraph())
... nx.add_path(G, [10, 11, 12])
... [
... len(c)
... for c in sorted(nx.weakly_connected_components(G), key=len, reverse=True)
... ] [4, 3]
If you only want the largest component, it's more efficient to use max instead of sort:
>>> largest_cc = max(nx.weakly_connected_components(G), key=len)See :
The following pages refer to to this document either explicitly or contain code examples using this.
networkx.algorithms.components.strongly_connected.strongly_connected_components
networkx.algorithms.components.connected.connected_components
networkx.algorithms.components.weakly_connected.weakly_connected_components
networkx.algorithms.components.weakly_connected.number_weakly_connected_components
networkx.algorithms.components.weakly_connected.is_weakly_connected
Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.
Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)
SVG is more flexible but power hungry; and does not scale well to 50 + nodes.
All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them