disjoint_union(G, H)
This algorithm forces distinct integer node labels.
A new graph is created, of the same class as G. It is recommended that G and H be either both directed or both undirected.
The nodes of G are relabeled 0 to len(G)-1, and the nodes of H are relabeled len(G) to len(G)+len(H)-1.
Graph, edge, and node attributes are propagated from G and H to the union graph. If a graph attribute is present in both G and H the value from H is used.
A NetworkX graph
Return the disjoint union of graphs G and H.
>>> G = nx.Graph([(0, 1), (0, 2), (1, 2)])
... H = nx.Graph([(0, 3), (1, 2), (2, 3)])
... G.nodes[0]["key1"] = 5
... H.nodes[0]["key2"] = 10
... U = nx.disjoint_union(G, H)
... U.nodes(data=True) NodeDataView({0: {'key1': 5}, 1: {}, 2: {}, 3: {'key2': 10}, 4: {}, 5: {}, 6: {}})
>>> U.edges EdgeView([(0, 1), (0, 2), (1, 2), (3, 4), (4, 6), (5, 6)])See :
The following pages refer to to this document either explicitly or contain code examples using this.
networkx.algorithms.operators.binary.union
networkx.algorithms.operators.binary.full_join
networkx.algorithms.operators.binary.disjoint_union
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