<｜begin▁of▁sentence｜>### Instruction:
Imagine you're crafting a strategy to enhance the user experience for our brand's social media community, by visualizing the interconnectedness amongst the users. To help conceptualize this network, could you craft a digital representation with the use of NetworkX, based on a specific model that mimics the organic growth of social networks?

This model should encapsulate the following attributes to reflect our user base accurately: we're looking at a network of 100 nodes to start with, and the probabilities for node connections should be guided by the parameters alpha at 0.41, representing the likelihood of connecting to a node based on popularity, beta at 0.54, signifying preference for attaching to nodes that are well-connected, and gamma at 0.05, denoting the inclination for forming connections between non-popular nodes. Additionally, please consider the factors delta_in at 0.2 and delta_out at 0.8 to respect the existing network's in-degree and out-degree.

To maintain consistency in our visualizations and corresponding insights, let's initialize the graph generation process with a seed value of 42. This will allow us to replicate the network structure if we need to review the strategy at a later stage. Could you integrate these specifications and produce a visual graph that we can analyze for our social media strategy sessions?

Should this visualization be saved and shared internally as a .gml file, please name it appropriately with reference to our project, for instance, "SocialMediaUserNetwork.gml".
### Response:
import networkx as nx

# Generate a scale-free graph
G = nx.scale_free_graph(n=100, alpha=0.41, beta=0.54, gamma=0.05, delta_in=0.2, delta_out=0.8, seed=42)

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