<｜begin▁of▁sentence｜>### Instruction:
Imagine we're crafting an intricate recipe designed to tantalize the taste buds of the mind, where the ingredients are vertices and the spices are the edges that connect them, creating a complex yet harmonious network of flavor. This particular dish on our menu is the Mycielski Graph, a specialty known for its nontrivial chromatic number that increases with each iteration, much like a sauce that becomes richer with each layer of seasoning.

For this fourth course in our mathematical degustation, I am keen to savor the specific arrangement of this graph's connections. Could you, as the chef behind this cognitive cuisine, meticulously prepare the Mycielski concoction at its fourth stage, presenting to me the detailed list of pairings – edges, as it were – that unite the elements? Additionally, I would relish the opportunity to appreciate the full scale of this creation by knowing the total count of nodes that comprise it.

This request is analogous to asking you to generate the 4th Mycielski Graph using NetworkX, a tool as essential in the mathematical kitchen as a sharp knife is in a culinary one, and furnish me with its edge list along with the node count. A meal for the mind, indeed.
### Response:
import networkx as nx

# Generate the n-th Mycielski Graph
n = 4
M = nx.mycielski_graph(n)

# Print the edges of the Mycielski Graph
print('Edges of the Mycielski Graph M{}:'.format(n), list(M.edges()))

# Print the number of nodes in the Mycielski Graph
print('Number of nodes in M{}:'.format(n), M.number_of_nodes())
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