<｜begin▁of▁sentence｜>### Instruction:
Given the Internet Graph which you can read from a GML file named as-22july06.gml, can you use the EVA algorithm in cdlib with randomly assigned node attributes to perform attributed community detection? And can you compute the purity, which is the product of the frequencies of the most frequent labels carried by the nodes within the communities?
### Response:
from cdlib.algorithms import eva
from cdlib import evaluation
import random
import networkx as nx

# Create a graph
G = nx.read_gml('as-22july06.gml')

# Define node attributes
labels = dict()
for node in G.nodes():
    labels[node] = {'label': random.choice(['A', 'B', 'C', 'D'])}

# Apply EVA algorithm for attributed community detection
communities = eva(G, labels, alpha=0.5)

# Evaluate the purity of the detected communities
pur = evaluation.purity(communities)
print(pur.score)
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