difference(G, H)
The node sets of H and G must be the same.
Attributes from the graph, nodes, and edges are not copied to the new graph. If you want a new graph of the difference 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 in H)
A NetworkX graph. G and H must have the same node sets.
Returns a new graph that contains the edges that exist in G but not in H.
>>> G = nx.Graph([(0, 1), (0, 2), (1, 2), (1, 3)])
... H = nx.Graph([(0, 1), (1, 2), (0, 3)])
... R = nx.difference(G, H)
... R.nodes NodeView((0, 1, 2, 3))
>>> R.edges EdgeView([(0, 2), (1, 3)])See :
The following pages refer to to this document either explicitly or contain code examples using this.
networkx.algorithms.operators.binary.difference
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