Rivista e Edizione

Volume 23 (2022): Edizione 1 (March 2022)

Volume 22 (2021): Edizione 1 (September 2021)

Volume 21 (2020): Edizione 2-2 (January 2020)

Volume 21 (2020): Edizione 2-1 (January 2020)

Volume 21 (2020): Edizione 2 (January 2020)

Volume 21 (2020): Edizione 2-3 (January 2020)

Volume 21 (2020): Edizione 1 (January 2020)

Volume 20 (2019): Edizione 4 (January 2019)

Volume 20 (2019): Edizione 3 (January 2019)

Volume 20 (2019): Edizione 2 (January 2019)

Volume 20 (2019): Edizione 1 (January 2019)

Volume 19 (2018): Edizione 1 (January 2018)

Volume 18 (2017): Edizione 1 (January 2017)

Volume 17 (2016): Edizione 1 (January 2016)

Volume 16 (2015): Edizione 1 (January 2015)

Volume 15 (2014): Edizione 1 (January 2014)

Volume 14 (2013): Edizione 1 (January 2013)

Volume 13 (2012): Edizione 1 (January 2012)

Volume 12 (2011): Edizione 1 (January 2011)

Volume 11 (2010): Edizione 1 (January 2010)

Volume 10 (2009): Edizione 1 (January 2009)

Dettagli della rivista
Formato
Rivista
eISSN
1529-1227
Pubblicato per la prima volta
31 Jan 2000
Periodo di pubblicazione
1 volta all'anno
Lingue
Inglese

Cerca

Volume 21 (2020): Edizione 2-1 (January 2020)

Dettagli della rivista
Formato
Rivista
eISSN
1529-1227
Pubblicato per la prima volta
31 Jan 2000
Periodo di pubblicazione
1 volta all'anno
Lingue
Inglese

Cerca

1 Articoli
Accesso libero

Geodesic Cycle Length Distributions in Delusional and Other Social Networks

Pubblicato online: 01 Oct 2020
Pagine: 1 - 42

Astratto

Abstract

A recently published paper [Martin (2017) JoSS 18(1):1-21] investigates the structure of an unusual set of social networks, those of the alternate personalities described by a patient undergoing therapy for multiple personality disorder (now known as dissociative identity disorder). The structure of these networks is modeled using the dk-series, a sequence of nested network distributions of increasing complexity. Martin finds that the first of these networks contains a striking feature of a large “hollow ring”; a cycle with no shortcuts, so that the shortest path between any two nodes in the cycle is along the cycle (in more precise graph theory terms, this is a geodesic cycle). However, the subsequent networks have much smaller largest cycles, smaller than those expected by the models. In this work, I re-analyze these delusional social networks using exponential random graph models (ERGMs) and investigate the distribution of the lengths of geodesic cycles. I also conduct similar investigations for some other social networks, both fictional and empirical, and show that the geodesic cycle length distribution is a macro-level structure that can arise naturally from the micro-level processes modeled by the ERGM.

Parole chiave

  • Geodesic cycle
  • Exponential random graph model
  • ERGM
  • -series random graphs
  • Social networks
  • Fictional networks
  • Dissociative identity disorder
1 Articoli
Accesso libero

Geodesic Cycle Length Distributions in Delusional and Other Social Networks

Pubblicato online: 01 Oct 2020
Pagine: 1 - 42

Astratto

Abstract

A recently published paper [Martin (2017) JoSS 18(1):1-21] investigates the structure of an unusual set of social networks, those of the alternate personalities described by a patient undergoing therapy for multiple personality disorder (now known as dissociative identity disorder). The structure of these networks is modeled using the dk-series, a sequence of nested network distributions of increasing complexity. Martin finds that the first of these networks contains a striking feature of a large “hollow ring”; a cycle with no shortcuts, so that the shortest path between any two nodes in the cycle is along the cycle (in more precise graph theory terms, this is a geodesic cycle). However, the subsequent networks have much smaller largest cycles, smaller than those expected by the models. In this work, I re-analyze these delusional social networks using exponential random graph models (ERGMs) and investigate the distribution of the lengths of geodesic cycles. I also conduct similar investigations for some other social networks, both fictional and empirical, and show that the geodesic cycle length distribution is a macro-level structure that can arise naturally from the micro-level processes modeled by the ERGM.

Parole chiave

  • Geodesic cycle
  • Exponential random graph model
  • ERGM
  • -series random graphs
  • Social networks
  • Fictional networks
  • Dissociative identity disorder

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