ITP OpenIR  > 理论物理所科研产出  > SCI论文
Zhang, P; Zhang, P (reprint author), Chinese Acad Sci, State Key Lab Theoret Phys, Inst Theoret Phys, Beijing 100190, Peoples R China.
Evaluating accuracy of community detection using the relative normalized mutual information
Source PublicationJOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
Language英语
KeywordRandom Graphs Networks Clustering Techniques
AbstractThe normalized mutual information (NMI) has been widely used to evaluate the accuracy of community detection algorithms. However in this article we show that the NMI is seriously affected by systematic errors due to finite size of networks, and may give a wrong estimate of performance of algorithms in some cases. We give a simple theory to the finite-size effect of NMI and test our theory numerically. Then we propose a new metric for the accuracy of community detection, namely the relative normalized mutual information (rNMI), which considers statistical significance of the NMI by comparing it with the expected NMI of random partitions. Our numerical experiments show that the rNMI overcomes the finite-size effect of the NMI.
2015
Issue0Pages:P11006
Subject AreaMechanics ; Physics
DOIhttp://dx.doi.org/10.1088/1742-5468/2015/11/P11006
Indexed BySCI
Funding OrganizationSanta Fe Institute ; Santa Fe Institute ; Santa Fe Institute ; Santa Fe Institute
Citation statistics
Document Type期刊论文
Identifierhttp://ir.itp.ac.cn/handle/311006/20803
Collection理论物理所科研产出_SCI论文
Corresponding AuthorZhang, P (reprint author), Chinese Acad Sci, State Key Lab Theoret Phys, Inst Theoret Phys, Beijing 100190, Peoples R China.
Recommended Citation
GB/T 7714
Zhang, P,Zhang, P . Evaluating accuracy of community detection using the relative normalized mutual information[J]. JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT,2015(0):P11006.
APA Zhang, P,&Zhang, P .(2015).Evaluating accuracy of community detection using the relative normalized mutual information.JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT(0),P11006.
MLA Zhang, P,et al."Evaluating accuracy of community detection using the relative normalized mutual information".JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT .0(2015):P11006.
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