<｜begin▁of▁sentence｜>### Instruction:
Let's consider the scenario where we're tasked with optimizing network connections within a professional organization, reminiscent of a network similar to that of a karate club. The objective here is to conduct an analysis that mirrors the strategic approach we often apply in tax planning, but instead of financial data, we're dissecting social interactions.

We would begin with a thorough examination of the organization's social network data, which has been meticulously compiled within a GML file named "karate.gml." Our strategy centers on a targeted review process akin to a beam search, with a focus on maximizing influential interactions. To do this effectively, our virtual "beam" must have a breadth of five, establishing a limitation on the number of connections we concurrently evaluate.

In translating this to the social network at hand, we aim to methodically traverse through the connections, or "edges," between members. However, rather than proceeding indiscriminately, we'll assign precedence based upon each member's "eigenvector centrality," a measure akin to weighing the relative importance of deductions or exemptions within a complex tax return. By focusing on those nodes with a higher eigenvector centrality, our analysis will concentrate on those individuals wielding the greatest influence within this social tapestry—effectively ensuring that our review is as strategic and efficient as our tax optimization endeavors. 

Could you provide guidance on how to embark on this selective navigation through the "karate.gml" network, adhering to the parameters of a beam search with a beam width set at five, whilst giving preferential treatment to the nodes which embody the epitome of centrality within this social hierarchy?
### Response:
import networkx as nx

G = nx.karate_club_graph()
centrality = nx.eigenvector_centrality(G)
source = 0
width = 5
beam_edges = list(nx.bfs_beam_edges(G, source, centrality.get, width))
print(beam_edges)
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