Edizioni

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 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

2 Articoli
Accesso libero

Imputation of missing network data: Some simple procedures

Pubblicato online: 10 Jan 2020
Pagine: 1 - 29

Astratto

Abstract

Analysis of social network data is often hampered by non-response and missing data. Recent studies show the negative effects of missing actors and ties on the structural properties of social networks. This means that the results of social network analyses can be severely biased if missing ties were ignored and only complete cases were analyzed. To overcome the problems created by missing data, several treatment methods are proposed in the literature: model-based methods within the framework of exponential random graph models, and imputation methods. In this paper we focus on the latter group of methods, and investigate the use of some simple imputation procedures to handle missing network data. The results of a simulation study show that ignoring the missing data can have large negative effects on structural properties of the network. Missing data treatment based on simple imputation procedures, however, does also have large negative effects and simple imputations can only successfully correct for non-response in a few specific situations.

Parole chiave

  • Missing data
  • Single imputation
  • Descriptive network analysis
  • Friendship network
Accesso libero

Change and External Events in Computer-Mediated Citation Networks: English Language Weblogs and the 2004 U.S. Electoral Cycle*

Pubblicato online: 10 Jan 2020
Pagine: 1 - 29

Astratto

Abstract

This study examines global patterns of stability and change within six longitudinal samples of English-language weblogs (or “blogs”) during the 2004 U.S. Presidential election campaign. Using distance-based methods of graph comparison, we explore the evolution of the blog-blog citation networks for each sample during the period. In addition to describing the qualitative dynamics of the blog networks, we relate major campaign events (e.g., party political conventions and debates) to the observed pace of change. As we demonstrate, such events are associated with substantial differences in overall network volatility; moreover, volatility is also shown to have strong seasonal and endogenous components. Our findings suggest that external factors (both regular and episodic) may be important drivers of network dynamics.

Parole chiave

  • blogs
  • political networks
  • dynamic networks
  • graph comparison
  • network visualization
2 Articoli
Accesso libero

Imputation of missing network data: Some simple procedures

Pubblicato online: 10 Jan 2020
Pagine: 1 - 29

Astratto

Abstract

Analysis of social network data is often hampered by non-response and missing data. Recent studies show the negative effects of missing actors and ties on the structural properties of social networks. This means that the results of social network analyses can be severely biased if missing ties were ignored and only complete cases were analyzed. To overcome the problems created by missing data, several treatment methods are proposed in the literature: model-based methods within the framework of exponential random graph models, and imputation methods. In this paper we focus on the latter group of methods, and investigate the use of some simple imputation procedures to handle missing network data. The results of a simulation study show that ignoring the missing data can have large negative effects on structural properties of the network. Missing data treatment based on simple imputation procedures, however, does also have large negative effects and simple imputations can only successfully correct for non-response in a few specific situations.

Parole chiave

  • Missing data
  • Single imputation
  • Descriptive network analysis
  • Friendship network
Accesso libero

Change and External Events in Computer-Mediated Citation Networks: English Language Weblogs and the 2004 U.S. Electoral Cycle*

Pubblicato online: 10 Jan 2020
Pagine: 1 - 29

Astratto

Abstract

This study examines global patterns of stability and change within six longitudinal samples of English-language weblogs (or “blogs”) during the 2004 U.S. Presidential election campaign. Using distance-based methods of graph comparison, we explore the evolution of the blog-blog citation networks for each sample during the period. In addition to describing the qualitative dynamics of the blog networks, we relate major campaign events (e.g., party political conventions and debates) to the observed pace of change. As we demonstrate, such events are associated with substantial differences in overall network volatility; moreover, volatility is also shown to have strong seasonal and endogenous components. Our findings suggest that external factors (both regular and episodic) may be important drivers of network dynamics.

Parole chiave

  • blogs
  • political networks
  • dynamic networks
  • graph comparison
  • network visualization

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