Identifying bias in cluster quality metrics

Abstract

We study potential biases of popular cluster quality metrics, such as conductance or modularity. We propose a method that uses both stochastic and preferential attachment block models construction to generate networks with preset community structures, 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.

References in corpus (13)

• Emergence of scaling in random networks
• Statistical mechanics of complex networks
• Community structure in social and biological networks
• Modularity and community structure in networks
• Community detection in graphs
• Resolution limit in community detection
• Community detection in networks: A user guide
• The ground truth about metadata and community detection in networks
• Hierarchical Block Structures and High-resolution Model Selection in Large Networks
• A Review of Stochastic Block Models and Extensions for Graph Clustering
• Analysis of Network Clustering Algorithms and Cluster Quality Metrics at Scale
• Community detection in multi-relational data with restricted multi-layer stochastic blockmodel
• Generative models for two-ground-truth partitions in networks