<｜begin▁of▁sentence｜>### Instruction:
Imagine you are tasked with investigating a network of insurance claimants, represented as a graphical map of connections, where claimants are nodes connected by their mutual claims. This network has four claimants: 0 and 1 both have claims marked with a 'red' flag for closer scrutiny, while 2 and 3 are flagged 'blue' indicating a different concern. Connections between claimants who share the same flag color are noted as pairs: (0, 1) denotes a connection between the 'red' claimants, while (2, 3) pairs up the 'blue' flagged claimants.

As part of your investigation, you are required to evaluate whether claims of the same color tend to be connected significantly more than expected by chance. This is essentially analyzing the attribute assortativity in the claimant network, specifically for the 'color' attribute using NetworkX. It is crucial to understand whether there's an inherent pattern or structure to these claims that could indicate fraudulent activity. Can you calculate the assortativity coefficient for the 'color' attribute in this claimant network to determine the extent to which like-color claims are connected?
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()

# Add nodes with a color attribute
G.add_nodes_from([0, 1], color='red')
G.add_nodes_from([2, 3], color='blue')

# Add edges between nodes of the same color
G.add_edges_from([(0, 1), (2, 3)])

# Calculate the attribute assortativity coefficient for the 'color' attribute
assortativity_coefficient = nx.attribute_assortativity_coefficient(G, 'color')

print('Attribute assortativity coefficient:', assortativity_coefficient)
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