<｜begin▁of▁sentence｜>### Instruction:
As a policy researcher, you are often tasked with mapping complex networks of stakeholders or policy elements, which may involve understanding the interconnectedness of different entities. Let's say you are modeling a network of collaborations between different departments within a policy-making institution, and you've compiled your data on these collaborations as a set of relationships or 'edges': [(1, 2), (1, 3), (2, 3), (3, 4), (4, 5)].

To further analyze the structure of these collaborations, you're interested in identifying the groups of departments that form tightly-knit clusters where each department has direct collaborations with all the others in the same cluster. In network analysis, these groups are referred to as cliques, and you are specifically interested in examining the cliques that cannot be extended by including an adjacent vertex — the so-called 'maximal cliques'.

For this purpose, could you elaborate on how one might use the NetworkX package to construct and visualize a new graph where each node represents one of these maximal cliques, with edges indicating overlap between cliques? The graphical representation of this 'maximal clique graph' would be a valuable tool for examining the cohesiveness of your network and identifying potential areas for strengthening interdepartmental collaboration.
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()
G.add_edges_from([(1, 2), (1, 3), (2, 3), (3, 4), (4, 5)])

# Create the maximal clique graph
max_clique_graph = nx.make_max_clique_graph(G, create_using=nx.Graph)

# Print the nodes of the maximal clique graph
print('Nodes of the maximal clique graph:', max_clique_graph.nodes())
# Print the edges of the maximal clique graph
print('Edges of the maximal clique graph:', max_clique_graph.edges())
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