<｜begin▁of▁sentence｜>### Instruction:
Given a social network graph (for example, Zachary's karate club), where each node represents a club member and each edge represents a tie between two members, can you calculate the non-randomness of the graph and its relative non-randomness with respect to Erdos Renyi random graphs, considering the graph is divided into 2 communities?
### Response:
import networkx as nx

# Create a graph using NetworkX's built-in karate club graph
G = nx.karate_club_graph()

# Calculate the non-randomness of the graph
# Assume that the graph has 2 communities
nr, nr_rd = nx.algorithms.non_randomness(G, k=2)

print('Non-randomness:', nr)
print('Relative non-randomness:', nr_rd)
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