<｜begin▁of▁sentence｜>### Instruction:
As a Database Administrator overseeing the structure and integrity of network data, you might come across a situation where you are tasked with analyzing the flow of information within a simplistic linear network consisting of five interconnected data nodes. This network, let's say, is stored in a GML file named "path_graph_5.gml." 

In this context, your objective could be to assess the relative significance of two critical nodes, labeled as '1' and '2,' as they relate to the transmission of data across this network. Specifically, you would be interested in quantifying the collective impact these nodes have in facilitating or controlling data flow within the network - a metric we'd refer to as the group betweenness centrality for the nodes in question.

In essence, you are being asked to evaluate the group betweenness centrality of nodes '1' and '2' within the linear network represented in "path_graph_5.gml." How would you proceed to calculate this particular measure of node influence and interpret its implications for data management and network efficiency?
### Response:
import networkx as nx

# Create a graph
G = nx.path_graph(5)

# Define a group of nodes
C = [1, 2]

# Calculate group betweenness centrality
betweenness = nx.group_betweenness_centrality(G, C, normalized=True, weight=None, endpoints=False)

print('Group betweenness centrality:', betweenness)
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