<｜begin▁of▁sentence｜>### Instruction:
Imagine you're a Physician Assistant, embarking on a unique research project to better understand how health information disseminates through social networks, particularly focusing on how new health practices are adopted and adapted within communities. For this exploration, consider a model consisting of 100 individuals represented as a complex network. Initially, each new individual connects with two others, mirroring the way people often turn to a couple of trusted sources for new health information. However, to realistically model social dynamics, you decide to add layers of complexity: there's a 10% chance that any new individual will form an additional connection, simulating the scenario where someone actively seeks out more health advice. Additionally, there's a 5% chance of existing connections being re-evaluated and potentially redirected, akin to someone changing their primary source of health advice based on new information or trust levels. 

Given this setup, you're interested in visualizing the structure of this network to analyze patterns that could inform effective health communication strategies. How would the described network graph, rooted in the extended Barabási–Albert model with the specified probabilities for adding and rewiring connections, manifest visually when mapped out? This visual representation could reveal insightful patterns, such as clusters of highly interconnected individuals or potential influencers, which are crucial for the dissemination of health information.
### Response:
import networkx as nx

# Parameters
n = 100 # Number of nodes
m = 2 # Each new node is connected to m existing nodes
p = 0.1 # Probability for adding a new edge
q = 0.05 # Probability for edge rewiring
seed = 42 # Seed for the random number generator

# Generate the graph
G = nx.extended_barabasi_albert_graph(n, m, p, q, seed)

# Draw the graph
import matplotlib.pyplot as plt
nx.draw(G, with_labels=True)
plt.show()
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