<｜begin▁of▁sentence｜>### Instruction:
Imagine you're assessing a group of properties, where each property's value depends on its relation to the other properties in the neighborhood. Think of 'A', 'B', 'C', and 'D' as individual properties, and the relationships between them as pathways influencing their respective market values. 'A' has a pathway to 'B', 'B' has a pathway to 'C', 'C' has a pathway back to 'A', and also 'C' has a pathway to 'D'.

In your role as a Property Appraiser, how would you utilize NetworkX to analyze the directional influence each property has on another and determine the trophic differences between these pathways? Keep in mind, this is akin to a circular dependency where the value of one property impacts another, which ultimately loops back. Understanding these differences could be pivotal in estimating the market position and value of each property within this networked community.
### Response:
import networkx as nx

# Create a directed graph
G = nx.DiGraph()

# Add edges
G.add_edge('A', 'B', weight=1)
G.add_edge('B', 'C', weight=1)
G.add_edge('C', 'A', weight=1)
G.add_edge('C', 'D', weight=1)

# Compute the trophic differences
trophic_diff = nx.algorithms.trophic.trophic_differences(G)

# Print the trophic differences
print(trophic_diff)
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