<｜begin▁of▁sentence｜>### Instruction:
Welcome, avid listeners, to another episode of "Graph Theory Simplified" here on your favorite podcast channel. Today, we have a fascinating subject that might tickle the intellect of our tech-savvy audience. Picture with me, if you will, the interconnected web of friendships within a social network, symbolized by pairs of individuals who share a bond: (1, 2), (1, 3), (2, 3), (3, 4), and (4, 5), not unlike the interactions amongst our very own podcast community.

In our episode, we're diving into the notion of quantifying the robustness of these connections, determining how tightly-knit our metaphorical social web truly is. For those engrossed in the beauty of graph theory, you might ask: How could one utilize the sophisticated toolkit provided by NetworkX, particularly the 'build_auxiliary_node_connectivity' function, to craft an auxiliary directed graph? This transformation is essential for our quest to compute the flow-based node connectivity, a measure that reveals the minimum number of individuals whose departure would disrupt the seamless flow of dialogue in our network.

So, dear listeners, imagine how this technical maneuver could give us insights into the resilience of our social fabric. But now, let us transition back to the real world, as we continue exploring the vast expanse of graph theory concepts. Stay tuned!
### Response:
import networkx as nx

# Create an undirected graph
G = nx.Graph()
G.add_edges_from([(1, 2), (1, 3), (2, 3), (3, 4), (4, 5)])

# Create auxiliary digraph to compute flow based node connectivity
H = nx.build_auxiliary_node_connectivity(G)

# Get mapping between original graph and auxiliary digraph
mapping = H.graph['mapping']

print('Auxiliary graph nodes:', H.nodes())
print('Node mapping:', mapping)
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