find_induced_nodes(G, s, t, treewidth_bound=9223372036854775807)
G must be a chordal graph and (s,t) an edge that is not in G.
If a treewidth_bound is provided, the search for induced nodes will end as soon as the treewidth_bound is exceeded.
The algorithm is inspired by Algorithm 4 in . A formal definition of induced node can also be found on that reference.
A chordal NetworkX graph
Source node to look for induced nodes
Destination node to look for induced nodes
Maximum treewidth acceptable for the graph H. The search for induced nodes will end as soon as the treewidth_bound is exceeded.
The algorithm does not support DiGraph, MultiGraph and MultiDiGraph. If the input graph is an instance of one of these classes, a NetworkXError
is raised. The algorithm can only be applied to chordal graphs. If the input graph is found to be non-chordal, a NetworkXError
is raised.
The set of induced nodes in the path from s to t in G
Returns the set of induced nodes in the path from s to t.
>>> G = nx.Graph()See :
... G = nx.generators.classic.path_graph(10)
... induced_nodes = nx.find_induced_nodes(G, 1, 9, 2)
... sorted(induced_nodes) [1, 2, 3, 4, 5, 6, 7, 8, 9]
The following pages refer to to this document either explicitly or contain code examples using this.
networkx.algorithms.chordal.find_induced_nodes
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