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

Tutti gli autori

• Gentile A.A. , Corallo A. , Bisconti C.G. , Fortunato L.

Titolo volume/Rivista

LECTURE NOTES IN COMPUTER SCIENCE

Anno di pubblicazione

2014

ISSN

0302-9743

ISBN

Non Disponibile

Numero di citazioni Wos

2

Ultimo Aggiornamento Citazioni

23/04/2018

Numero di citazioni Scopus

3

Ultimo Aggiornamento Citazioni

22/04/2018

Settori ERC

Non Disponibile

Codici ASJC

Non Disponibile