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Convegno della Società Italiana di Statistica

SIS 2017: New Statistical Developments in Data Science pp 27–38Cite as

Clustering Communities Using Interval K-Means

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Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 288))

Abstract

With regard to large networks there is a specific need to consider particular patterns relatable to structured groups of nodes which could be also defined as communities. In this work we will propose an approach to cluster the different communities using interval data. This approach is relevant in the context of the analysis of large networks and, in particular, in order to discover the different functionalities of the communities inside a network. The approach is shown in this paper by considering different examples of networks by means of synthetic data. The application is specifically related to a large network, that of the co-authorship network in Astrophysics.

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Notes

  1. 1.

    I thank the referees for their helpful suggestions

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Author information

Authors and Affiliations

  1. Universitá degli Studi di Roma “Niccoló Cusano”, Via Don Carlo Gnocchi 3, Rome, Italy

    Carlo Drago

Authors
  1. Carlo Drago
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Correspondence to Carlo Drago .

Editor information

Editors and Affiliations

  1. Dipartimento di Statistica, Informatica, Applicazioni ‘G. Parenti’ (DiSIA), Università degli Studi di Firenze, Florence, Italy

    Alessandra Petrucci

  2. Dipartimento Scienze Statistiche, Sapienza Università di Roma, Rome, Italy

    Filomena Racioppi

  3. Dipartimento di Matematica e Fisica, Università della Campania ‘Luigi Vanvitelli’, Caserta, Italy

    Rosanna Verde

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Drago, C. (2019). Clustering Communities Using Interval K-Means. In: Petrucci, A., Racioppi, F., Verde, R. (eds) New Statistical Developments in Data Science. SIS 2017. Springer Proceedings in Mathematics & Statistics, vol 288. Springer, Cham. https://doi.org/10.1007/978-3-030-21158-5_3

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