Zeitschriften und Ausgaben

Volumen 23 (2022): Heft 1 (March 2022)

Volumen 22 (2021): Heft 1 (September 2021)

Volumen 21 (2020): Heft 2-2 (January 2020)

Volumen 21 (2020): Heft 2-1 (January 2020)

Volumen 21 (2020): Heft 2 (January 2020)

Volumen 21 (2020): Heft 2-3 (January 2020)

Volumen 21 (2020): Heft 1 (January 2020)

Volumen 20 (2019): Heft 4 (January 2019)

Volumen 20 (2019): Heft 3 (January 2019)

Volumen 20 (2019): Heft 2 (January 2019)

Volumen 20 (2019): Heft 1 (January 2019)

Volumen 19 (2018): Heft 1 (January 2018)

Volumen 18 (2017): Heft 1 (January 2017)

Volumen 17 (2016): Heft 1 (January 2016)

Volumen 16 (2015): Heft 1 (January 2015)

Volumen 15 (2014): Heft 1 (January 2014)

Volumen 14 (2013): Heft 1 (January 2013)

Volumen 13 (2012): Heft 1 (January 2012)

Volumen 12 (2011): Heft 1 (January 2011)

Volumen 11 (2010): Heft 1 (January 2010)

Volumen 10 (2009): Heft 1 (January 2009)

Zeitschriftendaten
Format
Zeitschrift
eISSN
1529-1227
Erstveröffentlichung
31 Jan 2000
Erscheinungsweise
1 Hefte pro Jahr
Sprachen
Englisch

Suche

Volumen 15 (2014): Heft 1 (January 2014)

Zeitschriftendaten
Format
Zeitschrift
eISSN
1529-1227
Erstveröffentlichung
31 Jan 2000
Erscheinungsweise
1 Hefte pro Jahr
Sprachen
Englisch

Suche

1 Artikel
Uneingeschränkter Zugang

Choosing a Clustering: An A Posteriori Method for Social Networks

Online veröffentlicht: 14 Aug 2019
Seitenbereich: 1 - 21

Zusammenfassung

Abstract

Selecting an appropriate method of clustering for network data a priori can be a frustrating and confusing process. To address the problem we build on an a posteriori approach developed by Grimmer and King (2011) that compares hundreds of possible clustering methods at once through concise and intuitive visualization. We adapt this general method to the context of social networks, extend it with additional visualization features designed to enhance interpretability, and describe its principled use, outlining steps for selecting a class of methods to compare, interpreting visual output, and making a final selection. The interactive method, implemented in R, is demonstrated using Zachary’s karate club, a canonical dataset from the network literature.

Schlüsselwörter

  • clustering
  • networks
  • social networks
  • visualization
  • interactive
  • community detection
  • cluster analysis
  • comparing clusterings
1 Artikel
Uneingeschränkter Zugang

Choosing a Clustering: An A Posteriori Method for Social Networks

Online veröffentlicht: 14 Aug 2019
Seitenbereich: 1 - 21

Zusammenfassung

Abstract

Selecting an appropriate method of clustering for network data a priori can be a frustrating and confusing process. To address the problem we build on an a posteriori approach developed by Grimmer and King (2011) that compares hundreds of possible clustering methods at once through concise and intuitive visualization. We adapt this general method to the context of social networks, extend it with additional visualization features designed to enhance interpretability, and describe its principled use, outlining steps for selecting a class of methods to compare, interpreting visual output, and making a final selection. The interactive method, implemented in R, is demonstrated using Zachary’s karate club, a canonical dataset from the network literature.

Schlüsselwörter

  • clustering
  • networks
  • social networks
  • visualization
  • interactive
  • community detection
  • cluster analysis
  • comparing clusterings

Planen Sie Ihre Fernkonferenz mit Scienceendo