Attention on Weak Ties in Social and Communication Networks

  • Lilian Weng
  • Márton Karsai
  • Nicola Perra
  • Filippo Menczer
  • Alessandro FlamminiEmail author
Part of the Computational Social Sciences book series (CSS)


Granovetter’s weak tie theory of social networks is built around two central hypotheses. The first states that strong social ties carry the large majority of interaction events; the second maintains that weak social ties, although less active, are often relevant for the exchange of especially important information (e.g., about potential new jobs in Granovetter’s work). While several empirical studies have provided support for the first hypothesis, the second has been the object of far less scrutiny. A possible reason is that it involves notions relative to the nature and importance of the information that are hard to quantify and measure, especially in large scale studies. Here, we search for empirical validation of both Granovetter’s hypotheses. We find clear empirical support for the first. We also provide empirical evidence and a quantitative interpretation for the second. We show that attention, measured as the fraction of interactions devoted to a particular social connection, is high on weak ties—possibly reflecting the postulated informational purposes of such ties—but also on very strong ties. Data from online social media and mobile communication reveal network-dependent mixtures of these two effects on the basis of a platform’s typical usage. Our results establish a clear relationships between attention, importance, and strength of social links, and could lead to improved algorithms to prioritize social media content.



We would like to thank Albert-László Barabási for the mobile phone cell dataset used in this research, Twitter for providing public streaming data, and the Enron Email Analysis Project at UC Berkeley for cleaning up and sharing the Enron email dataset. MK acknowledges support from LABEX MiLyon. This work was partially funded by NSF grant CCF-1101743 and the James S. McDonnell Foundation.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Lilian Weng
    • 1
  • Márton Karsai
    • 2
  • Nicola Perra
    • 3
  • Filippo Menczer
    • 4
  • Alessandro Flammini
    • 4
    Email author
  1. 1.Affirm Inc.San FranciscoUSA
  2. 2.Univ Lyon, ENS de Lyon, Inria, CNRSLyonFrance
  3. 3.Centre for Business Networks AnalysisUniversity of GreenwichLondonUK
  4. 4.Center for Complex Networks and Systems Research, School of Informatics and ComputingIndiana UniversityBloomingtonUSA

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