This paper presents a novel consensus community detection (CCD) performed adopting a modularity-based community detection algorithm that exploits the concept of consensus over N independent trials to generate robust communities and to aggregate marginal nodes into a single community. The algorithm is tested on a class of artificial networks with built-in community structure that can be made to reflect the properties of real-world networks. Preliminary results show that CCD outperforms a single run of the original algorithm in terms of Normalised Mutual Information (NMI), number of communities and community size distribution, and provides an effective tool for community detection in real-world networks and a way to overcome the dependence on random seed of modularity-based algorithms.

Evaluation of the performance of a modularity-based consensus community detection algorithm

Fabio Morea
;
Domenico De Stefano
2023-01-01

Abstract

This paper presents a novel consensus community detection (CCD) performed adopting a modularity-based community detection algorithm that exploits the concept of consensus over N independent trials to generate robust communities and to aggregate marginal nodes into a single community. The algorithm is tested on a class of artificial networks with built-in community structure that can be made to reflect the properties of real-world networks. Preliminary results show that CCD outperforms a single run of the original algorithm in terms of Normalised Mutual Information (NMI), number of communities and community size distribution, and provides an effective tool for community detection in real-world networks and a way to overcome the dependence on random seed of modularity-based algorithms.
2023
9788891935632
https://it.pearson.com/content/dam/region-core/italy/pearson-italy/pdf/Docenti/Università/CLADAG-2023.pdf
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11368/3058139
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