Different Approaches to Community Detection

This chapter unfolds different aims underpinning community detection – in a relaxed form that includes assortative as well as disassortative group structures with dense and sparse internal connections, respectively – and discusses how the resulting problem perspectives relate to various applications...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Advances in Network Clustering and Blockmodeling S. 105 - 119
Hauptverfasser: Rosvall, Martin, Delvenne, Jean‐Charles, Schaub, Michael T., Lambiotte, Renaud
Format: Buchkapitel
Sprache:Englisch
Veröffentlicht: United Kingdom John Wiley & Sons, Incorporated 2020
John Wiley & Sons, Ltd
Schriftenreihe:Wiley Series in Computational and Quantitative Social Science
Schlagworte:
Assortative group structure
Clustering perspective
Community detection
Cut-based perspective
Disassortative group structure
Dynamical perspective
Stochastic block model perspective
ISBN:9781119224709, 1119224705, 1119483298, 9781119483298
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:This chapter unfolds different aims underpinning community detection – in a relaxed form that includes assortative as well as disassortative group structures with dense and sparse internal connections, respectively – and discusses how the resulting problem perspectives relate to various applications. It focuses on four broad perspectives that have served as motivation for community detection in the literature: the cut‐based perspective that minimizes a constraint such as the number of links between groups of nodes, the clustering perspective that maximizes internal density in groups of nodes, the stochastic block model perspective that identifies groups of nodes in which nodes are stochastically equivalent, and the dynamical perspective that identifies groups of nodes in which flows stay for a relatively long time such that they form building blocks of dynamics on networks. While this categorization is not unique, the authors believe that it can help clarify concepts about community detection.
ISBN:9781119224709
1119224705
1119483298
9781119483298
DOI:10.1002/9781119483298.ch4