# Combine (join) networkx Graphs

#### Tags: graph-theory, networkx, python

Say I have two networkx graphs, `G` and `H`:

```G=nx.Graph()
fromnodes=[0,1,1,1,1,1,2]
tonodes=[1,2,3,4,5,6,7]
for x,y in zip(fromnodes,tonodes):

H=nx.Graph()
fromnodes=range(2,8)
tonodes=range(8,14)
for x,y in zip(fromnodes,tonodes):
```

What is the best way to join the two networkx graphs?

I’d like to preserve the node names (note the common nodes, 2 to 7). When I used `nx.disjoint_union(G,H)`, this did not happen:

```>>> G.nodes()
[0, 1, 2, 3, 4, 5, 6, 7]
>>> H.nodes()
[2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
>>> Un= nx.disjoint_union(G,H)
>>> Un.nodes()
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]
#
```

The `H` node labels were changed (not what I want). I want to join the graphs at the nodes with the same number.

Note. This is not a duplicate of Combine two weighted graphs in NetworkX.

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 nx

G=nx.Graph()

H=nx.Graph()

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`.

Source: stackoverflow