<｜begin▁of▁sentence｜>### Instruction:
Good day,

Imagine we're evaluating a telemedicine network designed as a wheel graph with 10 nodes, each node representing a healthcare facility within a larger telehealth system. As part of optimizing the interconnectivity of these facilities for efficient communication, we're looking to transform this wheel graph into a chordal graph. A chordal graph, as you may know, is one where every cycle of four or more nodes has a chord, which is an edge that is not part of the cycle but connects two nodes of the cycle. 

To achieve such an optimization, could you advise on the method for calculating the chordal completion of this wheel graph utilizing NetworkX software? Additionally, it would be valuable to identify which specific edges we would need to introduce to convert our telemedicine network's graph into a chordal structure, thus enhancing the direct paths between facilities and potentially reducing the time for information to travel through the network.

Note, if there was any data file such as a GML involved in this scenario, it would be crucial to reference the file name, but as there isn't, we can proceed without it.

Thank you for your assistance in this matter, as it will greatly contribute to the improvement of our remote healthcare delivery system.
### Response:
import networkx as nx
from networkx.algorithms.chordal import complete_to_chordal_graph

# Create a wheel graph
G = nx.wheel_graph(10)

# Complete the graph to a chordal graph
H, alpha = complete_to_chordal_graph(G)

# Print the edges added to G
added_edges = set(H.edges()) - set(G.edges())
print('Added edges to complete to chordal graph:', added_edges)
<|EOT|>
