<｜begin▁of▁sentence｜>### Instruction:
As an aquaculture farmer, imagine you're keenly observing the social interactions within a population of fish in your care, which you've been documenting with the same meticulous approach used in the well-known "karate club" study of social networks. Your aquatic social network has been divided into two distinct tanks, representing two separate communities. You're curious about the structure of these interactions and particularly interested in assessing the degree of order within these social groups.

To achieve this, you wish to calculate the measure of non-randomness for the fish social network, operating under the assumption that there are two main schools of fish, akin to communities. Furthermore, you're looking to compare this observed structure to that of purely chance-based associations, similar to what one might find in randomly formed networks described by the Erdos-Renyi model. If the documentation of your fish's interactions was saved in a 'karate.gml' file, you'd want to know how the actual non-randomness of your aquacultural society stacks up against that you'd expect from a theoretical random distribution of social ties.

Could you reframe this complex task into a more tangible analysis, focused on the intricacies of your aquatic charges' social fabric, and gauge how much of the structure is potentially the result of genuine social preferences as opposed to random swim-paths?
### Response:
import networkx as nx

# Create a graph using a known model
G = nx.karate_club_graph()

# Compute the non-randomness of the graph
nr, nr_rd = nx.non_randomness(G, 2)

# Print the results
print('Non-randomness:', nr)
print('Relative non-randomness:', nr_rd)
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