attracting_components(G)
An attracting component in a directed graph G is a strongly connected component with the property that a random walker on the graph will never leave the component, once it enters the component.
The nodes in attracting components can also be thought of as recurrent nodes. If a random walker enters the attractor containing the node, then the node will be visited infinitely often.
To obtain induced subgraphs on each component use: (G.subgraph(c).copy() for c in attracting_components(G))
The graph to be analyzed.
If the input graph is undirected.
A generator of sets of nodes, one for each attracting component of G.
Generates the attracting components in G.
The following pages refer to to this document either explicitly or contain code examples using this.
networkx.algorithms.components.attracting.number_attracting_components
networkx.algorithms.components.attracting.is_attracting_component
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