<｜begin▁of▁sentence｜>### Instruction:
As a criminal psychologist, you might envision each node in the graph as an individual in a social network of criminal activity. Think of the edges as the influence or interaction between these individuals, where one may hold sway or pass on information to another. In this scenario, node 1 represents a key figure whose behavior and connections impact various subordinates or associates, denoted as nodes 2 through 5, with the interactions represented by the connections (1, 2), (1, 3), (2, 5), (3, 4), and (4, 5).

Now, to analyze the structures within this clandestine network and better comprehend the spread of influence or information, it's pivotal to map out the "dominance frontiers" for each participant starting from our central figure, node 1. This concept essentially reflects the extent to which each individual's influence reaches out within the web of interactions before meeting a point where other influences converge or dominate.

Understanding these frontiers will allow us to profile the network's hierarchy and dynamics, such as pinpointing which individuals are pivotal in disseminating orders or information from the central figure to peripheral nodes within the graph.

Could you rework this investigative network and translate its intricate web into a comprehensive layout of dominance frontiers by using NetworkX to simulate these relationships and influences starting from individual 1, our person of interest? This detailed map could prove instrumental in unravelling the complex interdependencies within this nefarious social structure.
### Response:
import networkx as nx

# Create a directed graph
G = nx.DiGraph()
G.add_edges_from([(1, 2), (1, 3), (2, 5), (3, 4), (4, 5)])

# Compute dominance frontiers
dominance = nx.dominance_frontiers(G, 1)
print(sorted((u, sorted(df)) for u, df in dominance.items()))
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