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International Conference on Research Challenges in Information Science

RCIS 2020: Research Challenges in Information Science pp 38–57Cite as

How to Measure Influence in Social Networks?

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Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 385))

Abstract

Today, social networks are a valued resource of social data that can be used to understand the interactions among people and communities. People can influence or be influenced by interactions, shared opinions and emotions. However, in the social network analysis, one of the main problems is to find the most influential people. This work aims to report on the results of literature review whose goal was to identify and analyse the metrics, algorithms and models used to measure the user influence on social networks. The search was carried out in three databases: Scopus, IEEEXplore, and ScienceDirect. We restricted published articles between the years 2014 until 2020, in English, and we used the following keywords: social networks analysis, influence, metrics, measurements, and algorithms. Backward process was applied to complement the search considering inclusion and exclusion criteria. As a result of this process, we obtained 25 articles: 12 in the initial search and 13 in the backward process. The literature review resulted in the collection of 21 influence metrics, 4 influence algorithms, and 8 models of influence analysis. We start by defining influence and presenting its properties and applications. We then proceed by describing, analysing and categorizing all that were found metrics, algorithms, and models to measure influence in social networks. Finally, we present a discussion on these metrics, algorithms, and models. This work helps researchers to quickly gain a broad perspective on metrics, algorithms, and models for influence in social networks and their relative potentialities and limitations.

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Acknowledgments

This work has been supported by IViSSEM: POCI-01-0145-FEDER-28284, COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020.

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Authors and Affiliations

  1. Centro ALGORITMI, University of Minho, 4800-058, Guimarães, Portugal

    Ana Carolina Ribeiro, Bruno Azevedo, Jorge Oliveira e Sá & Ana Alice Baptista

Authors
  1. Ana Carolina Ribeiro
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  2. Bruno Azevedo
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  3. Jorge Oliveira e Sá
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  4. Ana Alice Baptista
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Corresponding author

Correspondence to Ana Carolina Ribeiro .

Editor information

Editors and Affiliations

  1. Utrecht University, Utrecht, The Netherlands

    Fabiano Dalpiaz

  2. Stockholm University, Kista, Sweden

    Jelena Zdravkovic

  3. The Institute of Digital Innovation and Research, Dublin, Ireland

    Pericles Loucopoulos

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Ribeiro, A.C., Azevedo, B., Oliveira e Sá, J., Baptista, A.A. (2020). How to Measure Influence in Social Networks?. In: Dalpiaz, F., Zdravkovic, J., Loucopoulos, P. (eds) Research Challenges in Information Science. RCIS 2020. Lecture Notes in Business Information Processing, vol 385. Springer, Cham. https://doi.org/10.1007/978-3-030-50316-1_3

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  • DOI: https://doi.org/10.1007/978-3-030-50316-1_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-50315-4

  • Online ISBN: 978-3-030-50316-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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