<｜begin▁of▁sentence｜>### Instruction:
Imagine you're tackling a feature in a social network analysis application where the software you're developing needs to assess the connectivity strength between users based on shared connections within and across different user groups. In this case, the social network is represented as a graph with edges between users indicating a connection.

You have the following connections data between the users: `[(0, 1), (0, 2), (0, 3), (1, 4), (2, 4), (3, 4)]`. The users are grouped into communities with the following assignments: `{0: 0, 1: 1, 2: 0, 3: 0, 4: 0}`, where the key represents the user and the value represents the community ID.

Your task is to integrate an analysis feature using NetworkX that takes two user nodes, in this case, `user 0` and `user 4`, and calculates the proportion of shared connections they have within their own community (intra-community) in comparison to shared connections they have with users from different communities (inter-community).

The result is a ratio that could be useful for understanding how interconnected certain users are within their own community versus with external communities, which might be essential for implementing features such as community-specific news feeds or group recommendations. Can you rework this measure into the upcoming release of our network analysis toolkit?
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()
G.add_edges_from([(0, 1), (0, 2), (0, 3), (1, 4), (2, 4), (3, 4)])
# Add community information
G.nodes[0]['community'] = 0
G.nodes[1]['community'] = 1
G.nodes[2]['community'] = 0
G.nodes[3]['community'] = 0
G.nodes[4]['community'] = 0
# Compute WIC measure
preds = nx.within_inter_cluster(G, [(0, 4)])
for u, v, p in preds:
    print(f'({u}, {v}) -> {p:.8f}')
# (0, 4) -> 1.99800200
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