How to Choose Community Detection Methods in Complex Networks

Cécile Bothorel (), Laurent Brisson () and Inna Lyubareva ()
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Cécile Bothorel: IMT Atlantique - LUSSI - Département Logique des Usages, Sciences sociales et Sciences de l'Information - IMT Atlantique - IMT Atlantique - IMT - Institut Mines-Télécom [Paris], Lab-STICC_DECIDE - Equipe DECIDE - Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance - ENIB - École Nationale d'Ingénieurs de Brest - UBS - Université de Bretagne Sud - UBO - Université de Brest - ENSTA Bretagne - École Nationale Supérieure de Techniques Avancées Bretagne - IMT - Institut Mines-Télécom [Paris] - CNRS - Centre National de la Recherche Scientifique - UBL - Université Bretagne Loire - IMT Atlantique - IMT Atlantique - IMT - Institut Mines-Télécom [Paris]
Laurent Brisson: IMT Atlantique - LUSSI - Département Logique des Usages, Sciences sociales et Sciences de l'Information - IMT Atlantique - IMT Atlantique - IMT - Institut Mines-Télécom [Paris], Lab-STICC_DECIDE - Equipe DECIDE - Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance - ENIB - École Nationale d'Ingénieurs de Brest - UBS - Université de Bretagne Sud - UBO - Université de Brest - ENSTA Bretagne - École Nationale Supérieure de Techniques Avancées Bretagne - IMT - Institut Mines-Télécom [Paris] - CNRS - Centre National de la Recherche Scientifique - UBL - Université Bretagne Loire - IMT Atlantique - IMT Atlantique - IMT - Institut Mines-Télécom [Paris]
Inna Lyubareva: IMT Atlantique - LUSSI - Département Logique des Usages, Sciences sociales et Sciences de l'Information - IMT Atlantique - IMT Atlantique - IMT - Institut Mines-Télécom [Paris], LEGO - Laboratoire d'Economie et de Gestion de l'Ouest - UBS - Université de Bretagne Sud - UBO - Université de Brest - IMT - Institut Mines-Télécom [Paris] - IBSHS - Institut Brestois des Sciences de l'Homme et de la Société - UBO - Université de Brest - UBL - Université Bretagne Loire - IMT Atlantique - IMT Atlantique - IMT - Institut Mines-Télécom [Paris]

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Abstract: Discovering community structure in complex networks is a mature field since a tremendous number of community detection methods have been introduced in the literature. Nevertheless, it is still very challenging for practitioners to choose in each particular case the most suitable algorithm which would provide the richest insights into the structure of the social network they study. Through a case study of the French crowdfunding platform, Ulule, this paper demonstrates an original methodology for the selection of a relevant algorithm. For this purpose we, firstly, compare the partitions of 11 well-known algorithms. Then, bivariate map based on hub dominance and transitivity is used to identify the partitions which unveil communities with the most interesting size and internal topologies. These steps result in three community detection methods relevant for our data. Finally, we add the socioeconomic indicators, meaningful in the framework of the crowdfunding platform, in order to select the most significant algorithm of community detection, and to analyze the cooperation patterns among the platform's users and their impact on success of fundraising campaigns. In line with previous socioeconomic studies, we demonstrate that the social concept of homophily in online groups really matters. In addition, our approach puts in light that crowdfunding groups may benefit from diversity.

Keywords: Social Networks Analysis; Community Detection; Choice of method; Complex Networks; Online cooperation; Crowdfunding (search for similar items in EconPapers)
Date: 2021-06-22
New Economics Papers: this item is included in nep-net
Note: View the original document on HAL open archive server: https://hal.science/hal-03028871v1
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Citations: View citations in EconPapers (1)

Published in Series Computational Social Science, 2021, Methods and Applications in Social Networks Analysis. Evidence from Collaborative, governance, historical, and mobility networks, pp 16-36

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03028871

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