<｜begin▁of▁sentence｜>### Instruction:
Imagine we are tasked with conceptualizing a new social media platform, inspired by the natural growth patterns found in social networks. To simulate this evolution, we envision creating a prototype network model that mirrors the way users might connect. To capture this dynamic, I propose we utilize an advanced model based on the Barabási–Albert algorithm to design a network of 100 users (nodes).

In our model, let's set the stage where every new user that joins the platform doesn't just randomly make friends with any three existing users, but follows a strategic pattern, somewhat like users gravitating towards those with similar interests or larger followings. We'll infuse a touch of spontaneity by including a 10% chance that any two people (existing nodes) might strike up a connection or a 5% chance that some of the existing friendships (edges) could pivot to connect different people, reflecting real-world dynamics where interests and relationships evolve over time.

To maintain consistency in our user interaction simulation, we use a preset randomization pattern, or a 'seed', fixed at 42, which will allow us to replicate our findings or forecast trends.

Finally, we’ll visually map out this burgeoning online community which can be a powerful presentation tool to demonstrate our understanding of social dynamics and to ensure that the design of the platform is conducive to organic yet structured network growth.
### Response:
import networkx as nx

# Parameters for the extended Barabási–Albert graph
num_nodes = 100
num_edges = 3
p = 0.1
q = 0.05
seed = 42

# Create the graph using the extended Barabási–Albert model
G = nx.extended_barabasi_albert_graph(num_nodes, num_edges, p, q, seed)

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