NORMALIZED MUTUAL INFORMATION EXAGGERATES COMMUNITY DETECTION PERFORMANCE
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[1] Zhao Yang,et al. A Comparative Analysis of Community Detection Algorithms on Artificial Networks , 2016, Scientific Reports.
[2] Junfeng Hu,et al. On the relationship between Gaussian stochastic blockmodels and label propagation algorithms , 2014, Journal of Statistical Mechanics: Theory and Experiment.
[3] Yong-Yeol Ahn,et al. The Impact of Random Models on Clustering Similarity , 2017, bioRxiv.
[4] Leon Danon,et al. Comparing community structure identification , 2005, cond-mat/0505245.
[5] David W. Matula,et al. Extensions of maximum concurrent flow to identify hierarchical community structure and hubs in networks , 2008 .
[6] Farhad Shahrokhi,et al. The maximum concurrent flow problem , 1990, JACM.
[7] Leto Peel,et al. The ground truth about metadata and community detection in networks , 2016, Science Advances.
[8] Christine Nardini,et al. A corrected normalized mutual information for performance evaluation of community detection , 2016 .
[9] Cristopher Moore,et al. Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.
[10] James Bailey,et al. Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance , 2010, J. Mach. Learn. Res..