<｜begin▁of▁sentence｜>### Instruction:
Imagine you're examining a network of insurance claims, where each node represents a party involved in a claim, and each edge represents a connection or interaction between parties. Your current case file depicts a network with interactions listed as (1 to 2), (1 to 3), (2 to 4), and (2 to 5). In the insurance world, a clean, non-fraudulent scenario often resembles a tree structure, with no closed loops or multiple routes to the same claim.

As an insurance investigator, you would be keen to establish whether this network is, in fact, a tree – which would suggest a straightforward claims process without any red flags for fraudulent activity. 

Now, consider an additional piece of information that comes to light: a new interaction has been discovered between parties 3 and 4. With this new connection, you’ll need to reassess: does this network still maintain the characteristics of a tree, or does this new development suggest a more complex, potentially problematic web of claims?

For the purposes of your investigation, you may visualize this network using NetworkX software to efficiently analyze the structure. Think of it like a diagram drawn up during your investigation, marking a clear path of claim interactions that should not intersect nor loop back on themselves if they are to follow the pattern of a bona fide tree structure.
### Response:
import networkx as nx

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

# Check if G is a tree
is_tree = nx.is_tree(G)
print('G is a tree:', is_tree)

# Add another edge that creates a cycle
G.add_edge(3, 4)

# Check if G is still a tree
is_tree = nx.is_tree(G)
print('G is still a tree after adding an edge:', is_tree)
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