<｜begin▁of▁sentence｜>### Instruction:
As a physician specializing in data analysis, imagine you're tasked with assessing the health of a network of interconnected patients, where each patient represents a vertex, and their relationships signify edges. You've received a network diagram representing these connections, where patients (vertices) are linked by shared medical conditions (edges).

One particular metric you're interested in is the Dice similarity coefficient, which quantifies the similarity between pairs of patients based on their shared medical conditions. For instance, you're asked to compute the Dice similarity coefficient for pairs like Patient 0 and Patient 1, or Patient 1 and Patient 3.

To accomplish this, you'll need to utilize the NetworkX library, a powerful tool akin to a diagnostic instrument for analyzing networks. By leveraging NetworkX's functions, such as `dice_coefficient`, you can efficiently compute the similarity coefficients between specific pairs of patients, aiding in your assessment and treatment planning for this interconnected patient network.
### Response:
from igraph import Graph

# Create an undirected graph
g = Graph([(0, 1), (1, 2), (2, 3), (3, 4), (0, 4), (1, 3)], directed=False)

# Calculate Dice similarity coefficient
dice_similarity = g.similarity_dice(pairs=[(0, 1), (1, 3)])

# Print the result
print(dice_similarity)
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