<｜begin▁of▁sentence｜>### Instruction:
Imagine you're overseeing two separate marketing campaigns for a brand, each represented by a unique network of relationships and interactions. The first campaign has formed connections between entities 1 and 2 and then between 2 and 3, creating a sequence of engagements. Meanwhile, the second campaign has fostered its own series of interactions, linking entity 4 to 5 and subsequently 5 to 6.

In order to analyze the overall impact and combined outreach of these two distinct initiatives without intermingling their individual interactions, you'd want to merge these networks into a comprehensive overview. How might you approach crafting a unified network visualization by merging these two separate campaigns’ sequences of engagements, making sure each maintains its distinct cluster of connections in the joined representation?
### Response:
import networkx as nx

# Create two separate graphs
G1 = nx.Graph([(1, 2), (2, 3)])
G2 = nx.Graph([(4, 5), (5, 6)])

# Use disjoint_union_all to combine the graphs
U = nx.disjoint_union_all([G1, G2])

# Print the nodes and edges of the resulting graph
print(list(U.nodes()))
print(list(U.edges()))
<|EOT|>
