Work

Null-adjusted persistence function for high-resolution community detection

Year: 2025

Type: article

Abstract: Modularity and persistence probability are two widely used quality functions for detecting communities in complex networks. In this paper, we introduce a new objective function called null-adjusted persistence, which incorporates features from both modularity and persistence probability, as it implies a comparison of persistence probability with the same configuration null model of modularity. We prove key analytic properties of this new function, demonstrating that it successfully addresses modularity’s well-known scaling and resolution limitations, as well as the monotonic bias of persistence probability with respect to cluster size. To optimize this new function, we adapt the Louvain method and evaluate our approach on both synthetic benchmarks and real-world networks. Our results show that maximizing null-adjusted persistence consistently yields higher-resolution partitions than standard modularity maximization, particularly in large real networks.

Source: Information Sciences

DOI: 10.1016/j.ins.2025.123032

Authors: Alessandro Avellone, Paolo Bartesaghi, Stefano Benati, Christos Charalambous, Rosanna Grassi

Institutions: University of Milano-Bicocca, University of Milan, University of Trento, University of Cyprus

Language: English

Topic: Complex Network Analysis Techniques

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,200