<｜begin▁of▁sentence｜>### Instruction:
Picture this: In the intricate web of a recent case, we find ourselves looking at a connection diagram between certain key individuals and entities. The links on paper chart the interactions between five main players, Conceptually, these connections form a graph, with edges representing the links as such: [(1, 2), (2, 3), (3, 4), (4, 1), (1, 3)].

Now, to make sense of this web and to anticipate every possible direct connection that might arise without alerting anyone of our investigation, what we need is to convert this into a 'chordal graph' using some network analysis tools – specifically, NetworkX's algorithm that enriches this graph with additional links. The result would be a pattern where every cycle of four or more nodes has a chord, simplifying our task of predicting interactions.

Moreover, while we're enhancing our intelligence network, we'd also benefit from knowing the optimal 'elimination ordering' of nodes, which can give us a strategic path for peeling back the layers of this social network one individual at a time.

In summary, our task is to transform the current known interactions into a chordal graph and identify the sequence in which individuals should be approached or investigated for maximal efficiency. The intrigue lies in applying NetworkX to complete this task. How would you proceed with this covert operation?
### Response:
import networkx as nx
from networkx.algorithms.chordal import complete_to_chordal_graph

# Create a graph
G = nx.Graph()
G.add_cycle([1, 2, 3, 4])
G.add_edge(1, 3)

# Make the graph chordal
H, alpha = complete_to_chordal_graph(G)

print('Chordal Graph Edges:', H.edges())
print('Elimination Ordering:', alpha)
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