On the interpretability of thresholded social networks

Oren Tsur, David Lazer

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Understanding the factors of network formation is a fundamental aspect in the study of social dynamics. Online activity provides us with abundance of data that allows us to reconstruct and study social networks. Statistical inference methods are often used to study network formation. Ideally, statistical inference allows the researcher to study the significance of specific factors to the network formation. One popular framework is known as Exponential Random Graph Models (ERGM) which provides principled and statistically sound interpretation of an observed network structure. Networks, however, are not always given set in stone. Often times, the network is "reconstructed" by applying some thresholds on the observed data/signals. We show that subtle changes in the thresholding have significant effects on the ERGM results, casting doubts on the interpretability of the model. In this work we present a case study in which different thresholding techniques yield radically different results that lead to contrastive interpretations. Consequently, we revisit the applicability of ERGM to thresholded networks.

Original languageEnglish
Title of host publicationProceedings of the 11th International Conference on Web and Social Media, ICWSM 2017
PublisherAAAI press
Pages680-683
Number of pages4
ISBN (Electronic)9781577357889
StatePublished - 1 Jan 2017
Externally publishedYes
Event11th International Conference on Web and Social Media, ICWSM 2017 - Montreal, Canada
Duration: 15 May 201718 May 2017

Publication series

NameProceedings of the 11th International Conference on Web and Social Media, ICWSM 2017

Conference

Conference11th International Conference on Web and Social Media, ICWSM 2017
Country/TerritoryCanada
CityMontreal
Period15/05/1718/05/17

ASJC Scopus subject areas

  • Computer Networks and Communications

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