intersection(G, H)
Attributes from the graph, nodes, and edges are not copied to the new graph. If you want a new graph of the intersection of G and H with the attributes (including edge data) from G use remove_nodes_from() as follows
>>> G = nx.path_graph(3) >>> H = nx.path_graph(5) >>> R = G.copy() >>> R.remove_nodes_from(n for n in G if n not in H) >>> R.remove_edges_from(e for e in G.edges if e not in H.edges)
A NetworkX graph. G and H can have different node sets but must be both graphs or both multigraphs.
If one is a MultiGraph and the other one is a graph.
Returns a new graph that contains only the nodes and the edges that exist in both G and H.
>>> G = nx.Graph([(0, 1), (0, 2), (1, 2)])
... H = nx.Graph([(0, 3), (1, 2), (2, 3)])
... R = nx.intersection(G, H)
... R.nodes NodeView((0, 1, 2))
>>> R.edges EdgeView([(1, 2)])See :
The following pages refer to to this document either explicitly or contain code examples using this.
networkx.algorithms.operators.binary.intersection
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