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Détails du magazine
Format
Magazine
eISSN
1529-1227
Première publication
31 Jan 2000
Période de publication
1 fois par an
Langues
Anglais

Chercher

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

Détails du magazine
Format
Magazine
eISSN
1529-1227
Première publication
31 Jan 2000
Période de publication
1 fois par an
Langues
Anglais

Chercher

1 Articles
Accès libre

Choosing a Clustering: An A Posteriori Method for Social Networks

Publié en ligne: 14 Aug 2019
Pages: 1 - 21

Résumé

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.

Mots clés

  • clustering
  • networks
  • social networks
  • visualization
  • interactive
  • community detection
  • cluster analysis
  • comparing clusterings
1 Articles
Accès libre

Choosing a Clustering: An A Posteriori Method for Social Networks

Publié en ligne: 14 Aug 2019
Pages: 1 - 21

Résumé

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.

Mots clés

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

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