Abstract

The problem and implications of community detection in networks have raised a huge attention, for its important applications in both natural and social sciences. A number of algorithms has been developed to solve this problem, addressing either speed optimization or the quality of the partitions calculated. In this paper we propose a multi-step procedure bridging the fastest, but less accurate algorithms (coarse clustering), with the slowest, most effective ones (refinement). By adopting heuristic ranking of the nodes, and classifying a fraction of them as `critical', a refinement step can be restricted to this subset of the network, thus saving computational time. Preliminary numerical results are discussed, showing improvement of the final partition.

Autore Pugliese

• Corallo Angelo , Lazoi Mariangela

Tutti gli autori

• Corallo A. , Lazoi M. , Lettera S. , Marra M. , Verardi S.

Titolo volume/Rivista

UNIVERSAL JOURNAL OF INDUSTRIAL AND BUSINESS MANAGEMENT

Anno di pubblicazione

2014

ISSN

2332-3329

ISBN

Non Disponibile

Numero di citazioni Wos

Nessuna citazione

Ultimo Aggiornamento Citazioni

Non Disponibile

Numero di citazioni Scopus

Non Disponibile

Ultimo Aggiornamento Citazioni

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Settori ERC

Non Disponibile

Codici ASJC

Non Disponibile