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Sometimes Less Is More: When Aggregating Networks Masks Effects

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Complex Networks and Their Applications XI (COMPLEX NETWORKS 2016 2022)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1077))

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Abstract

A large body of research aims to detect the spread of something through a social network. This research often entails measuring multiple kinds of relationships among a group of people and then aggregating them into a single social network to use for analysis. The aggregation is typically done by taking a union of the various tie types. Although this has intuitive appeal, we show that in many realistic cases, this approach adds sufficient error to mask true network effects. We show that this can be the case, and demonstrate that the problem depends on: (1) whether the effect diffuses generically or in a tie-specific way, and (2) the extent of overlap between the measured network ties. Aggregating ties when diffusion is tie-specific and overlap is low will negatively bias and potentially mask network effects that are in fact present.

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Notes

  1. 1.

    This is distinct from the notion of overlap that refers to the extent to which two nodes share the same neighbors (see [15, 17]).

  2. 2.

    For a case in point, see [14], which shows that evidence of behavior spreading through the network is masked when the seven types of ties are aggregated. When disaggregated, the authors find evidence consistent with specific diffusion along the most intimate types of ties.

  3. 3.

    In rare instances, researchers instead or also look at the different networks separately; see [2, 14].

  4. 4.

    For the derivation of this result for OLS see [10] and for logistic regression see [19].

  5. 5.

    The magnitude of \(\beta \) affects the severity of the attenuation bias, not whether it is present or not.

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Author information

Authors and Affiliations

  1. Vanderbilt University, Nashville, TN, 37240, USA

    Jennifer M. Larson

  2. Meta, Menlo Park, CA, 94025, USA

    Pedro L. Rodríguez

Authors
  1. Jennifer M. Larson
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  2. Pedro L. Rodríguez
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Correspondence to Jennifer M. Larson .

Editor information

Editors and Affiliations

  1. University of Burgundy, Dijon, France

    Hocine Cherifi

  2. Dipartimento di Fisica e Chimica Emilio Segrè, Università degli Studi Palermo, Palermo, Italy

    Rosario Nunzio Mantegna

  3. Thomas J. Watson College of Engineering and Applied Science, Binghamton University, Binghamton, NY, USA

    Luis M. Rocha

  4. IUT Lumière - Université Lyon 2, University of Lyon, Bron, France

    Chantal Cherifi

  5. Dipartimento di Fisica e Chimica Emilio Segrè, Università degli Studi Palermo, Palermo, Italy

    Salvatore Miccichè

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Larson, J.M., Rodríguez, P.L. (2023). Sometimes Less Is More: When Aggregating Networks Masks Effects. In: Cherifi, H., Mantegna, R.N., Rocha, L.M., Cherifi, C., Miccichè, S. (eds) Complex Networks and Their Applications XI. COMPLEX NETWORKS 2016 2022. Studies in Computational Intelligence, vol 1077. Springer, Cham. https://doi.org/10.1007/978-3-031-21127-0_18

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  • DOI: https://doi.org/10.1007/978-3-031-21127-0_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21126-3

  • Online ISBN: 978-3-031-21127-0

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