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Erschienen in:
Cognitive Cascades within Media Ecosystems: Simulating Fragmentation, Selective Exposure and Media Tactics to Investigate Polarization Kapitel lesen Erstes Kapitel lesen

2023 | OriginalPaper | Buchkapitel

Sometimes Less Is More: When Aggregating Networks Masks Effects

verfasst von : Jennifer M. Larson, Pedro L. Rodríguez

Erschienen in: Complex Networks and Their Applications XI

Verlag: Springer International Publishing

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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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Vorheriges Kapitel Random Walk for Generalization in Goal-Directed Human Navigation on Wikipedia
Nächstes Kapitel An Adaptive Network Model Simulating the Effects of Different Culture Types and Leader Qualities on Mistake Handling and Organisational Learning
Fußnoten
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
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
In rare instances, researchers instead or also look at the different networks separately; see [2, 14].
 
4
For the derivation of this result for OLS see [10] and for logistic regression see [19].
 
5
The magnitude of \(\beta \) affects the severity of the attenuation bias, not whether it is present or not.
 
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Metadaten
Titel
Sometimes Less Is More: When Aggregating Networks Masks Effects
verfasst von
Jennifer M. Larson
Pedro L. Rodríguez
Copyright-Jahr
2023
Buch
Complex Networks and Their Applications XI

Print ISBN: 978-3-031-21126-3

Electronic ISBN: 978-3-031-21127-0

Copyright-Jahr: 2023

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

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