<｜begin▁of▁sentence｜>### Instruction:
Imagine we're handling the narrative of a bustling network in a manuscript—a lively town where story arcs intersect as our protagonists, denoted by the numerals 0 to 3, forge connections, much like the partnerships you and I foster between authors and the publishing world. Within the bustling narrative network, our dynamic pairings are like chapters outlined by edges; they connect as follows: Chapters (0, 1), (0, 2), (1, 3), and (2, 3) are interlinked plots within our literary tome.

In this vibrant town of tales, familial bonds mirror the community ties—kinship among characters. Characters 0 and 1 hail from the storied House of Community 0, a lineage steeped in rich backstory, while character 2 ventures solo, aligned with the enigmatic House of Community 1. And not to leave the twists hanging—character 3, too, shares allegiance with the House of Community 0.

Enter the intriguing concept of a 'resource allocation index'—a theoretical tool, if you will, akin to the careful calculus we undertake when determining market potential and readership for a manuscript. For our scenario, we would ponder the collaborative potential between our lead characters, 0 and 3—who share a deeply-rooted communal lineage. The crucial question is this: How might one compute the resource allocation index using the community information, specifically for the duo of characters (0 and 3) within our narrative’s framework?

Much like we analyze the strength and potential reach of a manuscript in the literary marketplace, this index would offer insights into the depth and strength of the connection between these two characters, considering the overlapping social circles to which they belong in our story’s universe.
### Response:
import networkx as nx

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

# Add community information
G.nodes[0]['community'] = 0
G.nodes[1]['community'] = 0
G.nodes[2]['community'] = 1
G.nodes[3]['community'] = 0

# Compute the resource allocation index
preds = nx.ra_index_soundarajan_hopcroft(G, [(0, 3)])

# Print the results
for u, v, p in preds:
    print(f'({u}, {v}) -> {p:.8f}')
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