<｜begin▁of▁sentence｜>### Instruction:
In the realm of digital publication, imagine we're crafting an article focused on the dynamics of social networks. Within the piece, we want to incorporate a discussion about how relationships can evolve from bidirectional interactions to unidirectional influences. To illustrate this concept, we've been provided with a dataset characterizing a rudimentary social network through a series of bilateral connections: 

- 'A' has a connection with 'B'
- 'A' is also connected to 'C'
- 'B' and 'C' share a connection
- 'C' extends a connection to 'D'

Now, let's consider a hypothetical scenario where these connections become directional, transforming the original undirected network into a directed one, indicative of a one-way flow of communication or influence.

For visual clarity and enhanced understanding, we seek to utilize NetworkX, a Python library adept at handling complex networks, to create a directed representation of our initial undirected network. The task at hand is to generate a directed view of this social graph and effectively list the edges of this newly oriented network so that we can further discuss the implications in our article. Could you explain how one might go about converting our provided network into such a directed view using NetworkX and then retrieving the edge list for the resulting directed graph?
### Response:
import networkx as nx

# Create an undirected graph
G = nx.Graph()
# Add some edges
e = [('A', 'B'), ('A', 'C'), ('B', 'C'), ('C', 'D')]
G.add_edges_from(e)

# Create a directed view of the graph
D = G.to_directed()

# Display edges of directed graph
print(list(D.edges()))
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