<｜begin▁of▁sentence｜>### Instruction:
Imagine you are a Business Analyst tasked with optimizing the collaborative efficiency between two departments within a company, let's call them Team G and Team H. Both teams work on projects that sometimes overlap. You've been given data regarding the project partnerships within each team, represented by pairs of team members who have collaborated on a project.

For Team G, the partnerships are as follows: (0, 1), (0, 2), (1, 2), (1, 3). For Team H, the collaborative pairs are: (0, 1), (1, 2), (0, 3). Your goal is to identify exclusive partnerships within Team G that are not mirrored in Team H, ensuring that both teams consist of the same members.

Could you leverage NetworkX to construct a visualization or schematic that would showcase those unique collaborative efforts present solely in Team G's dynamic that aren't found in Team H's cooperative structure, under the prerequisite that both teams incorporate an identical roster of members? This would assist in pinpointing specific relationships that may be leveraged or require further development to enhance inter-departmental synergy.
### Response:
import networkx as nx

# Create two graphs G and H with the same node sets
G = nx.Graph([(0, 1), (0, 2), (1, 2), (1, 3)])
H = nx.Graph([(0, 1), (1, 2), (0, 3)])

# Calculate the difference between G and H
R = nx.difference(G, H)

# Output the nodes and edges of the resulting graph
print('Nodes in the difference graph:', R.nodes())
print('Edges in the difference graph:', R.edges())
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