<｜begin▁of▁sentence｜>### Instruction:
Given a list of degrees from a network's degree distribution, how can you use igraph's power_law_fit function to fit a power-law to the data and determine the most suitable parameters such as the scaling parameter (alpha), the lower bound for the power-law behavior (xmin), and the goodness of fit measured by the Kolmogorov-Smirnov statistic (D)?
### Response:
import igraph as ig

# Assuming we have degree data for a network
data = [3, 3, 4, 5, 6, 7, 7, 8, 9, 10, 11, 12]

# Fit a power-law distribution to the data
result = ig.statistics.power_law_fit(data, xmin=None, method='auto', p_precision=0.01)

# Print the results of the power-law fitting
print('Alpha:', result['alpha'])
print('Xmin:', result['xmin'])
print('KS Statistic:', result['D'])
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