ArticleNature communications2025
Normalized mutual information is a biased measure for classification and community detection.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- Generative psychometrics via AI-GENIE: Automatic item generation and validation with network-integrated evaluation.Behavior research methods · 2026Article
- Information theory for hypergraph similarity.Science advances · 2026Article
- An improved Newman fast division algorithm based on multi-factor correlation for dynamic traffic sub-region control.PloS one · 2026Article
- Normalized mutual information is a biased measure for classification and community detection.Nature communications · 2025Article
- Quantifying community evolution in temporal networks.Scientific reports · 2025Article
- Detailed-level modelling of influence spreading on complex networks.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Normalized mutual information is widely used as a similarity measure for evaluating the performance of clustering and classification algorithms. In this paper, we argue that results returned by the normalized mutual information are biased for two reasons: first, because they ignore the information content of the contingency table and, second, because their symmetric normalization introduces spurious dependence on algorithm output. We introduce a modified version of the mutual information that remedies both of these shortcomings. As a practical demonstration of the importance of using an unbiased measure, we perform extensive numerical tests on a basket of popular algorithms for network community detection and show that one's conclusions about which algorithm is best are significantly affected by the biases in the traditional mutual information.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.