<｜begin▁of▁sentence｜>### Instruction:
Imagine we're examining the communication pathway within a network of four individuals, each represented as a node numbered from 0 to 3. These individuals have established conversational links in such a way that there's a sequence of dialogue passing from individual 0 to individual 1, then to individual 2, followed by individual 3, and there's also direct feedback from individual 3 to individual 0. Additionally, individual 1 has the capability to communicate directly with individual 3. 

Now, if we aim to identify the smallest number of these individuals whose temporary absence from the conversational loop would create a blockade, preventing the flow of dialogue from individual 0 to individual 3 using the most efficient method of dialogue replacement analogous to the shortest augmenting path in network flow, who would they be? 

This scenario does not pertain to an existing .gml file but rather uses data provided in the abstract to illustrate the network of conversations.
### Response:
import networkx as nx
from networkx.algorithms.connectivity import minimum_st_node_cut
from networkx.algorithms.connectivity import build_auxiliary_node_connectivity
from networkx.algorithms.flow import build_residual_network
from networkx.algorithms.flow import shortest_augmenting_path

# Create a directed graph
G = nx.DiGraph()
G.add_edges_from([(0, 1), (1, 2), (2, 3), (3, 0), (1, 3)])

# Use shortest_augmenting_path as the flow function
flow_func = shortest_augmenting_path

# Build the auxiliary graph and the residual network
H = build_auxiliary_node_connectivity(G)
R = build_residual_network(H, 'capacity')

# Find the minimum cutset
cutset = minimum_st_node_cut(G, s=0, t=3, flow_func=flow_func, auxiliary=H, residual=R)

print(cutset)
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