Paper Info
Title
Evaluation of Community Structures using Kappa Index and F-Score instead of Normalized Mutual Information.
Abstract
Community structures are critical towards understanding not only the network topology but also how the network functions. However, how to evaluate the quality of detected community structures is still challenging and remains unsolved. The most widely used metric, normalized mutual information (NMI), was proved to have finite size effect, and its improved form rNMI has reverse finite size effect. cNMI is thus proposed and has neither finite size effect nor reverse finite size effect. However, in this paper we show that cNMI violates the so-called proportionality assumption. In addition, NMI-type metrics have the problem of ignoring importance of small communities. Finally, they cannot be used to evaluate a single community of interest. In this paper, we map the computed community labels to the ground-truth ones through integer linear programming, then use Kappa index and F-score to evaluate the detected community structures. Experimental results demonstrate the rationality of our method.
Year
Venue
Field
2018
arXiv: Social and Information Networks
F1 score,Data mining,Community of interest,Kappa,Rationality,Computer science,Normalized mutual information,Network topology,Theoretical computer science,Proportionality (mathematics),Integer programming
DocType
Volume
Citations 
Journal
abs/1807.01130
0
PageRank 
References 
Authors
0.34
4
3
Name
Order
Citations
PageRank
Xin Liu12318.64
Hui-Min Cheng202.03
Zhong-Yuan Zhang3142.95