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Theories for Influencer Identification in Complex Networks

  • Sen Pei
  • Flaviano Morone
  • Hernán A. Makse
Chapter
Part of the Computational Social Sciences book series (CSS)

Abstract

In social and biological systems, the structural heterogeneity of interaction networks gives rise to the emergence of a small set of influential nodes, or influencers, in a series of dynamical processes. Although much smaller than the entire network, these influencers were observed to be able to shape the collective dynamics of large populations in different contexts. As such, the successful identification of influencers should have profound implications in various real-world spreading dynamics such as viral marketing, epidemic outbreaks, and cascading failure. In this chapter, we first summarize the centrality-based approach in finding single influencers in complex networks, and then discuss the more complicated problem of locating multiple influencers from a collective point of view. Progress rooted in collective influence theory, belief-propagation, and computer science will be presented. Finally, we present some applications of influencer identification in diverse real-world systems, including online social platforms, scientific publication, brain networks, and socioeconomic systems.

Notes

Acknowledgements

We acknowledge funding from NIH-NIBIB 1R01EB022720, NIH-NCI U54CA137788 / U54CA132378 and NSF-IIS 1515022.

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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Environmental Health Sciences, Mailman School of Public HealthColumbia UniversityNew YorkUSA
  2. 2.Levich Institute and Physics DepartmentCity College of New YorkNew YorkUSA

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