<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

Choosing a Clustering: An A Posteriori Method for Social Networks

Wharton School of the University of Pennsylvania

Journal of Social Structure

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Choosing a Clustering: An A Posteriori Method for Social Networks"}

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...

Choosing a Clustering: An A Posteriori Method for Social Networks

Online veröffentlicht: 14. Aug. 2019

Seitenbereich: 1 - 21

DOI: https://doi.org/10.21307/joss-2019-022

Schlüsselwörter
clustering, networks, social networks, visualization, interactive, community detection, cluster analysis, comparing clusterings

© 2019 Samuel D. Pimentel et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Choosing a Clustering: An A Posteriori Method for Social Networks

Online veröffentlicht: 14. Aug. 2019

Seitenbereich: 1 - 21

DOI: https://doi.org/10.21307/joss-2019-022

Schlüsselwörterclustering, networks, social networks, visualization, interactive, community detection, cluster analysis, comparing clusterings

© 2019 Samuel D. Pimentel et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
clustering, networks, social networks, visualization, interactive, community detection, cluster analysis, comparing clusterings