  Quantifying the amount of polarization is crucial for understanding and
studying political polarization in political and social systems. Several
methods are used commonly to measure polarization in social networks by purely
inspecting their structure. We analyse eight of such methods and show that all
of them yield high polarization scores even for random networks with similar
density and degree distributions to typical real-world networks. Further, some
of the methods are sensitive to degree distributions and relative sizes of the
polarized groups. We propose normalization to the existing scores and a minimal
set of tests that a score should pass in order for it to be suitable for
separating polarized networks from random noise. The performance of the scores
increased by 38%-220% after normalization in a classification task of 203
networks. Further, we find that the choice of method is not as important as
normalization, after which most of the methods have better performance than the
best-performing method before normalization. This work opens up the possibility
to critically assess and compare the features and performance of structural
polarization methods.
