<｜begin▁of▁sentence｜>### Instruction:
Imagine we're examining the intricate tapestry of courtly fashion during the reign of Louis XIV, where the connections between different influencers of style can be seen as a delicate web, much like the fine links of lace on the garments they once wore. Our goal is to unravel who amongst the court held sway over sartorial norms, akin to discovering the most pivotal figures in our historical fashion network. 

To achieve this, we will employ a method akin to tracing the threads of influence through time. Think of NetworkX as our scholarly needle, deftly navigating through the fabric of relationships. We have already documented interactions which can be likened to the stitches that hold the fabric together, represented by pairs such as the influential seamstress and the queen ('0, 1'), the queen and her protege ('1, 2'), and the protege's influence spreading to the aristocracy ('2, 3').

To assess who held the most clout in this historical network of fashion influencers, we will calculate each figure's Katz centrality. It is akin to measuring how the soft whispers of trendsetters echo through the grand halls of Versailles, while the attenuation factor alpha shall be as delicate as the balance between opulence and poise, determined by the inverse of the most robust chord in our metaphorical harpsichord of connections—the largest eigenvalue.

Could you guide us in applying this sophisticated method using NetworkX, ensuring we remain true to the authenticity of our period study? In this scenario, we do not possess a gml file but rather the raw data of interactions as delineated above, ready to be placed within the context of our courtly network analysis.
### Response:
import networkx as nx
import math

# Create a graph
G = nx.Graph()
# Add edges
G.add_edges_from([(0, 1), (1, 2), (2, 3)])

# Calculate the largest eigenvalue of the adjacency matrix
largest_eigenvalue = max(nx.adjacency_spectrum(G))
alpha = 1 / largest_eigenvalue

# Compute the Katz centrality
katz_centrality = nx.katz_centrality_numpy(G, alpha=alpha)

# Print the Katz centrality of each node
for node, centrality in sorted(katz_centrality.items()):
    print(f"Node {node}: Katz centrality = {centrality:.2f}")
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