<｜begin▁of▁sentence｜>### Instruction:
Imagine you're managing the online community of a bustling e-commerce platform, similar to how you manage product listings and interactions with customers on your site. In this digital landscape, you have a core team of five key team members, labeled as 0 through 4, who are instrumental in connecting with other niche vendors within your network—think of these vendors as a 'karate club,' where each member brings something unique to the mix.

To optimize your network's performance and ensure these key players are effectively engaging with the broader community, you're interested in assessing their combined influence. As the E-commerce Manager, you'd specifically want to know the extent to which these five team members collectively reach out to the rest of the community members. 

Could you provide an analysis expressed as a percentage indicating how many of these non-core team vendors are, in fact, engaged with your selected team of five? This data may be available in a graph format, typically reflected in a GML file, that represents the interactions within your online community network. By understanding this, you can better strategize on ways to expand your market reach and improve vendor relationships.
### Response:
import networkx as nx

# Create a graph
G = nx.karate_club_graph()

# Define the group of nodes for which we want to find the group degree centrality
S = [0, 1, 2, 3, 4]

# Calculate the group degree centrality
centrality = nx.group_degree_centrality(G, S)

print('Group degree centrality:', centrality)
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