<｜begin▁of▁sentence｜>### Instruction:
Imagine we're in the process of designing a cutting-edge social networking platform, featuring a visualization that maps the connections between various users. To achieve this, we have two datasets outlining connections between users during different activities.

The first dataset, Graph G, encapsulates interactions from a collaborative project and includes connections [(0, 1), (0, 2), (1, 2)]. The second dataset, Graph H, represents connections formed during a team-building retreat, with edges [(0, 3), (1, 2), (2, 3)].

To enhance the user experience with meaningful insight, we need to create a refined network visualization. This new design, Graph R, should only display the relationships that were consistent across both activities—the overlap. Could you devise a method using NetworkX to extract the essence of these connections, merging Graph G and Graph H to produce Graph R, which exhibits only the shared bonds between these nodes? This would allow us to elegantly illustrate the enduring relationships within our platform's community.
### Response:
import networkx as nx

# Create the first graph
G = nx.Graph()
G.add_edges_from([(0, 1), (0, 2), (1, 2)])

# Create the second graph
H = nx.Graph()
H.add_edges_from([(0, 3), (1, 2), (2, 3)])

# Compute the intersection of graphs G and H
R = nx.intersection(G, H)

print('Nodes in the intersection:', R.nodes())
print('Edges in the intersection:', R.edges())
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