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Identifying bias in network clustering quality metrics.
- Source :
- PeerJ Computer Science; Aug2023, p1-18, 18p
- Publication Year :
- 2023
-
Abstract
- We study potential biases of popular network clustering quality metrics, such as those based on the dichotomy between internal and external connectivity. We propose a method that uses both stochastic and preferential attachment block models construction to generate networks with preset community structures, and Poisson or scale-free degree distribution, to which quality metrics will be applied. These models also allow us to generate multi-level structures of varying strength, which will show if metrics favour partitions into a larger or smaller number of clusters. Additionally, we propose another quality metric, the density ratio. We observed that most of the studied metrics tend to favour partitions into a smaller number of big clusters, even when their relative internal and external connectivity are the same. The metrics found to be less biased are modularity and density ratio. [ABSTRACT FROM AUTHOR]
- Subjects :
- STOCHASTIC models
DENSITY
Subjects
Details
- Language :
- English
- ISSN :
- 23765992
- Database :
- Complementary Index
- Journal :
- PeerJ Computer Science
- Publication Type :
- Academic Journal
- Accession number :
- 171836983
- Full Text :
- https://doi.org/10.7717/peerj-cs.1523