<｜begin▁of▁sentence｜>### Instruction:
Attention all units, we've received a report indicating that we need to deploy resources to analyze the communication flow within our emergency response network. Our network diagram has identified the following paths of interaction between units:
- Unit 0 to Unit 1
- Unit 1 to Unit 2
- Unit 2 to Unit 0
- Unit 2 to Unit 3
- Unit 3 to Unit 0
- Unit 3 to Unit 1

We need to assess the patterns of coordination between these units by running a triadic census on this directed graph. It's crucial that we understand the different types of three-unit relationships present in our network to optimize response times and resource allocation.

Let's input this data and see if we can single out any critical triads, where information might be bottlenecking or where there's an established chain of command that could affect our unit's synergies during emergency scenarios. This will help us to anticipate potential communication breakdowns and reinforce our team's effectiveness in the field. Keep me updated with the statistics of the triadic relationships found within these connections. Over.
### Response:
import networkx as nx

# Create a directed graph
G = nx.DiGraph()
# Add edges
G.add_edges_from([(0, 1), (1, 2), (2, 0), (2, 3), (3, 0), (3, 1)])
# Compute the triadic census
triadic_census = nx.triadic_census(G)
# Print the triadic census
for key, value in triadic_census.items():
    print(f"{key}: {value}")
<|EOT|>
