<｜begin▁of▁sentence｜>### Instruction:
As a criminal investigator, imagine that you are tasked with analyzing a network of criminal connections. The graph represents the relationships between different individuals involved in criminal activities. Using the `dyad_census` function in your investigative toolkit, you need to classify each pair of individuals into mutual, asymmetric, or null categories based on their connections. This classification will help you understand the nature of the relationships between individuals within the network and potentially uncover key insights to aid your investigation.
### Response:
from igraph import Graph

# Create a directed graph with edges
g = Graph(directed=True)
g.add_vertices(4)
g.add_edges([(0, 1), (1, 2), (2, 3), (3, 0), (1, 3)])

# Perform dyad census
dyad_census_result = g.dyad_census()

# Print the dyad census result
print('Mutual:', dyad_census_result.mutual, '\nAsymmetric:', dyad_census_result.asym, '\nNull:', dyad_census_result.null)
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