Combine (join) networkx Graphs

The function you're looking for is compose, which produces a graph with all the edges and all the nodes that are in both graphs. If both graphs have a node with the same name, then a single copy ends up in the new graph. Similarly if the same edge exists in both. Here's an example, including edge/node attributes:

import networkx as nxG=nx.Graph()G.add_node(1, weight = 2)G.add_node(2, weight = 3)G.add_edge(1,2, flux = 5)G.add_edge(2,4)H=nx.Graph()H.add_node(1, weight = 4)H.add_edge(1,2, flux = 10)H.add_edge(1,3) F = nx.compose(G,H)#F has all nodes & edges of both graphs, including attributes#Where the attributes conflict, it uses the attributes of H.G.nodes(data=True)> NodeDataView({1: {'weight': 2}, 2: {'weight': 3}, 4: {}})H.nodes(data=True)> NodeDataView({1: {'weight': 4}, 2: {}, 3: {}})F.nodes(data=True)> NodeDataView({1: {'weight': 4}, 2: {'weight': 3}, 4: {}, 3: {}})G.edges(data=True)> EdgeDataView([(1, 2, {'flux': 5}), (2, 4, {})])H.edges(data=True)> EdgeDataView([(1, 2, {'flux': 10}), (1, 3, {})])F.edges(data=True)EdgeDataView([(1, 2, {'flux': 10}), (1, 3, {}), (2, 4, {})])

These preserve attributes, but obviously if there is a conflict this is not possible. The attributes of H take precedence.

There are also other options to do the symmetric difference, intersection, ...

If you have multiple graphs to join together, you can use compose_all, which just wraps a for loop around compose.

This did it.

   U=nx.Graph()   U.add_edges_from(G.edges()+H.edges())   U.add_nodes_from(G.nodes()+H.nodes()) #deals with isolated nodes

or, preserving the edge attributes:

   U.add_edges_from(G.edges(data=True)+H.edges(data=True))

and, to also preserve the node attributes:

   U.add_nodes_from(G.nodes(data=True)+H.nodes(data=True))

In case you want to add graph H to G then return G, you can use update method.