<｜begin▁of▁sentence｜>### Instruction:
As a web developer, imagine you're tasked with creating an interactive online tool that visualizes networks for a social platform. As part of the network analysis features, you want to calculate the engagement potential between pairs of users. Specifically, you're interested in implementing an algorithm that evaluates the likelihood of interaction between two users based on their mutual friends (common neighbors) and their influence within the network (centrality).

In this scenario, your platform's network is highly interconnected, much like a "complete" web of users where everyone is connected to everyone else. You're focusing on calculating this interaction potential for two specific pairs of users: User 0 with User 1, and User 2 with User 3. The tool should take into account not only how many mutual friends these pairs share but also how central those common friends are within the user network. You'll be utilizing what is known as the Common Neighbor and Centrality based Parameterized Algorithm (CCPA) with an influence factor (alpha) set to the standard level of 0.8.

To provide a real-world context to this calculation, let's say these engagement scores will help in tailoring user experience on the site, perhaps by suggesting new friend connections or prioritizing content in their feed. Your task is to embed this CCPA score calculation into your web application's backend logic to enhance its social interaction predictions. As the network is a simple unweighted graph with 5 nodes and includes all the possible connections, the scores you compute will play a critical role in how users discover and interact with each other on the platform.
### Response:
import networkx as nx

# Create a complete graph with 5 nodes
G = nx.complete_graph(5)

# Compute the Common Neighbor and Centrality based Parameterized Algorithm (CCPA) score
# for the pairs of nodes (0, 1) and (2, 3)
preds = nx.common_neighbor_centrality(G, [(0, 1), (2, 3)])

# Iterate through the CCPA scores and print them
for u, v, p in preds:
    print(f'({u}, {v}) -> {p}')
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