<｜begin▁of▁sentence｜>### Instruction:
Given an undirected graph with edges [(0,1), (0,2), (0,3), (1,4), (2,4), (3,4)] and community assignment where nodes 0, 2, 3, and 4 belong to community 0 and node 1 belongs to community 1, can you compute the within- and inter-cluster common neighbor ratio for the node pair (0, 4) using the NetworkX within_inter_cluster function?
### 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 information to the 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 ratio of within- and inter-cluster common neighbors
preds = nx.within_inter_cluster(G, [(0, 4)])

# Iterate through the results
for u, v, p in preds:
  print(f'({u}, {v}) -> {p:.8f}')
<|EOT|>
