ABSTRACT Network provides a flexible structure for capturing interactions among nodes. Clustering graphs into distinct classes based on their similarities yields valuable insights across various domains, such as social network analysis, biological network modelling and transportation systems. In this article, we introduce CIBM, a C lustering algorithm that leverages the rescaled I nterlacing B alance M easure to cluster networks. We further apply our method to multi‐layer network community detection, achieving significantly higher accuracy than non‐clustered approaches. The performance of our method is demonstrated through both numerical simulations and real data analysis.
Zhu et al. (Sun,) studied this question.