<｜begin▁of▁sentence｜>### Instruction:
Imagine you're overseeing a promotional campaign for a new product, where the flow of information is akin to a network of contacts passing along the message. You have a list showing who has been contacting whom, depicted as directed connections: Person 1 reaches out to Person 2, then Person 2 to Person 3, and so on, creating a sequence of contacts (1 to 2, 2 to 3, 3 to 4, 2 to 4, 3 to 1).

To refine your strategy and maximize the effectiveness of your campaign, you want to analyze the pattern of communication in this network. Specifically, you are interested in assessing how frequently well-connected influencers are reaching out to those who are equally central or peripheral in receiving information.

In technical terms, you're asking for the calculation of the degree assortativity coefficient, considering the 'out' degree (akin to how much an individual is reaching out or 'promoting') of the source contacts and the 'in' degree (akin to how much an individual is being targeted or 'marketed to') of the target contacts. This measurement will help you understand the propensity of similar-level influencers and targets to connect within your promotional network, thereby giving you insights on how to adjust your marketing tactics.
### Response:
import networkx as nx

# Create a directed graph
G = nx.DiGraph()
# Add edges
G.add_edges_from([(1, 2), (2, 3), (3, 4), (2, 4), (3, 1)])
# Calculate degree assortativity coefficient
r = nx.degree_assortativity_coefficient(G, x='out', y='in')
print(f"Degree assortativity coefficient: {r:3.3f}")
<|EOT|>
