<｜begin▁of▁sentence｜>### Instruction:
Imagine we're working on a new vehicle diagnostics system, intending to understand the importance of various sensor nodes within a simplified, circular-type sensor network, analogous to a four-node cycle graph. We're planning to utilize a method akin to Katz centrality to discern the relative influence of each sensor node.

To achieve a nuanced analysis, it's key that we adjust the Katz centrality calculation by establishing the attenuation factor, or 'alpha,' to a value just a hair's breadth less than the reciprocal of the largest eigenvalue derived from our network's adjacency matrix.

Could you craft a process to calculate the Katz centrality for each node within this four-point sensor network, ensuring alpha is fine-tuned as described? It's like tuning the suspension system to ensure optimal performance just before reaching its limit. Keep in mind, this assessment will greatly aid in refining our vehicle's sensor data reliability and overall systems efficiency.
### Response:
import networkx as nx
import math
# Create an undirected graph
G = nx.Graph()
# Add edges
G.add_edges_from([(0, 1), (1, 2), (2, 3), (3, 0)])
# Katz centrality calculation
# We use 1 over the largest eigenvalue of the adjacency matrix minus a small constant as alpha
phi = (1 + math.sqrt(5)) / 2.0  # largest eigenvalue of a cycle with 4 nodes
alpha = 1 / phi - 0.01
centrality = nx.katz_centrality(G, alpha=alpha)
# Output the centrality values
for node, centrality_value in sorted(centrality.items()):
    print(f'Node {node}: {centrality_value:.2f}')
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