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Is Normalized Mutual Information a Fair Measure for Comparing Community Detection Methods?

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ASONAM '15: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015

Pages 1584 - 1585

https://doi.org/10.1145/2808797.2809344

Published: 25 August 2015 Publication History

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Abstract

Normalized mutual information (NMI) is a widely used measure to compare community detection methods. Recently, however, the need of adjustment for information theoretic based measures has been argued because of their tendency in choosing clustering solutions with more communities. In this paper an experimental evaluation is performed to investigate this problem, and an adjustment that scales the values of NMI is proposed. Experiments on synthetic generated networks highlight the unbiased behavior of scaled NMI.

References

[1]

S. Romano, J. Bailey, V. Nguyen, and K. Verspoor. Standardized mutual information for clustering comparisons: One step further in adjustment for chance. In Proc. 31st Int. Conf. on Mach. Learn. JMLR W&CP 32 (1), pp. 1143--1151, 2014.

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[2]

N. X. Vinh, J. Epps, and J. Bailey. Information theoretic measures for clusterings comparison: Is a correction for chance necessary? In Proc. 26th Annual Int. Conf. on Mach. Learn., pp. 1073--1080, 2009.

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[3]

N. X. Vinh, J. Epps, and J. Bailey. Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance. J. Mach. Learn. Res., 11:2837--2854, 2010.

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Mahmoudi AJemielniak D(2024)Proof of biased behavior of Normalized Mutual InformationScientific Reports10.1038/s41598-024-59073-914:1Online publication date: 19-Apr-2024
https://doi.org/10.1038/s41598-024-59073-9
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ASONAM '15: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015

August 2015

835 pages

ISBN:9781450338547

DOI:10.1145/2808797

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 25 August 2015

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August 25 - 28, 2015

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Using mutual information as a cocitation similarity measure

On the Effects of Similarity in Community Detection

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