kosaraju_strongly_connected_components(G, source=None)
Uses Kosaraju's algorithm.
A directed graph.
If G is undirected.
A generator of sets of nodes, one for each strongly connected component of G.
Generate nodes in strongly connected components of graph.
Generate a sorted list of strongly connected components, largest first.
>>> G = nx.cycle_graph(4, create_using=nx.DiGraph())
... nx.add_cycle(G, [10, 11, 12])
... [
... len(c)
... for c in sorted(
... nx.kosaraju_strongly_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 = max(nx.kosaraju_strongly_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.strongly_connected.kosaraju_strongly_connected_components
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