<｜begin▁of▁sentence｜>### Instruction:
As a private investigator specializing in data analysis, I often employ sophisticated tools and techniques to uncover crucial information for my clients. Recently, I've been tasked with investigating a complex network involving individuals and their connections. Utilizing advanced graph analysis methods is essential in untangling the intricate web of relationships.

One particular challenge involves identifying all the interconnected triangles within the network. These triangles signify tightly knit groups or associations that could hold significant implications for my investigation. To accomplish this task, I plan to utilize the `list_triangles` function within the NetworkX Python library, which specializes in graph analysis and manipulation.

By inputting the edge set provided [(0, 1), (1, 2), (2, 0), (1, 3), (3, 4), (4, 1)] into the `list_triangles` function, I aim to extract all the triangular formations within the network. This analysis will help reveal clusters of individuals closely connected to one another, potentially shedding light on hidden alliances, patterns of behavior, or key influencers within the network.

With the insights gained from this analysis, I can provide my clients with valuable intelligence to support their decisions, mitigate risks, or even uncover crucial evidence in legal proceedings.
### Response:
from igraph import Graph

# Define the edges for the graph
edges = [(0, 1), (1, 2), (2, 0), (1, 3), (3, 4), (4, 1)]

# Create the graph with the defined edges
g = Graph(edges=edges)

# List all the triangles in the graph
triangles = g.list_triangles()

print(triangles)
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