<｜begin▁of▁sentence｜>### Instruction:
Suppose we are crafting an educational program to promote the understanding of how diseases can spread through a network of individuals, which we'll metaphorically represent using the structure of an icosahedral graph. This graph reflects a connected community with 20 members and 30 relationships between them. We want to evaluate the resilience of this community to the spread of a hypothetical disease by determining the minimum number of individuals that need to be removed to disconnect the transmission paths within the community. To make our simulation more precise, we would like to incorporate an advanced analytical method, specifically the shortest augmenting path algorithm, to model the flow of information (or in our case, disease transmission) through this network. How can we compute this metric of community resilience using NetworkX, and can we specify the shortest augmenting path algorithm as our chosen flow computation technique in our simulation setup?
### Response:
import networkx as nx
from networkx.algorithms.flow import shortest_augmenting_path

# Create a graph
G = nx.icosahedral_graph()

# Compute the node connectivity of the graph
connectivity = nx.node_connectivity(G, flow_func=shortest_augmenting_path)

print('Node connectivity of the icosahedral graph:', connectivity)
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