USI-AUC : an evaluation criterion of community detection based on a novel link-prediction method

Yiteng Wu, Hongtao Yu, Jianpeng Zhang, Shuxin Liu, Ruiyang Huang, Peng Li

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)
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Abstract

Modularity Evaluation (ME) is usually used in community detection for evaluating the disjoint and overlapping communities. In this paper, two obvious defects of ME are revealed and proved, including the non-decreasing contribution of isolated nodes to modularity and lacking of appropriate measures on overlapping community. We also propose a new evaluation criterion, the USI-AUC, which is the Area Under the Curve (AUC), originated from link-prediction of Uniform-Structure-Information (USI) model. We test the new criterion on various datasets, and find that such criterion can avoid the issues exposed in ME.

Original languageEnglish
Pages (from-to)439-462
Number of pages24
JournalIntelligent Data Analysis
Volume22
Issue number2
DOIs
Publication statusPublished - 14 Mar 2018

Fingerprint

Community Detection
Information Structure
Modularity
Defects
Curve
Prediction
Evaluation
Overlapping
Disjoint
Vertex of a graph
Community

Keywords

  • Evaluation of communities
  • link prediction
  • modularity evaluation
  • USI model
  • USI-AUC criterion

Cite this

Wu, Yiteng ; Yu, Hongtao ; Zhang, Jianpeng ; Liu, Shuxin ; Huang, Ruiyang ; Li, Peng. / USI-AUC : an evaluation criterion of community detection based on a novel link-prediction method. In: Intelligent Data Analysis. 2018 ; Vol. 22, No. 2. pp. 439-462.
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USI-AUC : an evaluation criterion of community detection based on a novel link-prediction method. / Wu, Yiteng; Yu, Hongtao; Zhang, Jianpeng; Liu, Shuxin; Huang, Ruiyang; Li, Peng.

In: Intelligent Data Analysis, Vol. 22, No. 2, 14.03.2018, p. 439-462.

Research output: Contribution to journalArticleAcademicpeer-review

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