<｜begin▁of▁sentence｜>### Instruction:
Given a graph whose edge set is [(0, 1), (0, 2), (0, 3), (1, 4), (2, 4), (3, 4)] with nodes belonging to two different communities, can you compute the ratio of within- and inter-cluster common neighbors for the node pair (0, 4) using NetworkX?
### 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)])
# Assign community attributes to nodes
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 the within-inter cluster ratio
preds = nx.within_inter_cluster(G, [(0, 4)])
# Print the WIC measures
for u, v, p in preds:
    print(f'({u}, {v}) -> {p:.8f}')
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