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Latent Ties Identification in Inter-Firms Social Networks

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New Perspectives in Statistical Modeling and Data Analysis

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Abstract

Social networks are usually analyzed through manifest variables. However there are social latent aspects that strongly qualify such networks. This paper aims to propose a statistical methodology to identify latent variable in inter-firm social networks. A multidimensional scaling technique is proposed to measure a latent variable as a combination of an appropriate set of two or more manifest relational aspects. This method, tested on an inter-firm social network in the Marche region (Italy), it is a new way to grasp social aspect with quantitative tools that could be implemented under several different conditions, using also other variables.

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Author information

Authors and Affiliations

  1. Dipartimento di Istituzioni Economiche e Finanziarie, Università di Macerata, Via Crescimbeni 20 – 21, 62100, Macerata, Italy

    Patrizia Ameli

  2. Dipartimento di Istituzioni Economiche e Finanziarie, Università di Macerata, Via Crescimbeni 20 – I, 62100, Macerata, Italy

    Federico Niccolini

  3. Dipartimento di Scienze Relazionali “Gustavo Iacono”, Università di Napoli Federico II, Via Porta di Massa 1, 80133, Napoli, Italy

    Francesco Palumbo

Authors
  1. Patrizia Ameli
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  2. Federico Niccolini
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  3. Francesco Palumbo
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Corresponding author

Correspondence to Patrizia Ameli .

Editor information

Editors and Affiliations

  1. Fac. Economia, Università di Catania, Corso Italia 55, Catania, 95129, Italy

    Salvatore Ingrassia

  2. Dipto. SEFEMEQ, Università di Roma - Tor Vergata, Via Columbia 2, Roma, 00133, Italy

    Roberto Rocci

  3. Dipto. Statistica, Probabilità e, Statistiche Applicate, Università di Roma, La Sapienza, Piazzale Aldo Moro 5, Roma, 00185, Italy

    Maurizio Vichi

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Ameli, P., Niccolini, F., Palumbo, F. (2011). Latent Ties Identification in Inter-Firms Social Networks. In: Ingrassia, S., Rocci, R., Vichi, M. (eds) New Perspectives in Statistical Modeling and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11363-5_21

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