Simulating systematic bias in attributed social networks and its effect on rankings of minority nodes

Abstract Network analysis provides powerful tools to learn about a variety of social systems. However, most analyses implicitly assume that the considered relational data is error-free, and reliable and accurately reflects the system to be analysed. Especially if the network consists of multiple gro...

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Autores principales:	Leonie Neuhäuser, Felix I. Stamm, Florian Lemmerich, Michael T. Schaub, Markus Strohmaier
Formato:	article
Lenguaje:	EN
Publicado:	SpringerOpen 2021
Materias:	Edge uncertainty Rankings Attributed networks Bias Social networks Applied mathematics. Quantitative methods T57-57.97
Acceso en línea:	https://doaj.org/article/8031096ebc0b46e7a56d0f09181872d0
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spelling	oai:doaj.org-article:8031096ebc0b46e7a56d0f09181872d02021-11-07T12:19:04ZSimulating systematic bias in attributed social networks and its effect on rankings of minority nodes10.1007/s41109-021-00425-z2364-8228https://doaj.org/article/8031096ebc0b46e7a56d0f09181872d02021-11-01T00:00:00Zhttps://doi.org/10.1007/s41109-021-00425-zhttps://doaj.org/toc/2364-8228Abstract Network analysis provides powerful tools to learn about a variety of social systems. However, most analyses implicitly assume that the considered relational data is error-free, and reliable and accurately reflects the system to be analysed. Especially if the network consists of multiple groups (e.g., genders, races), this assumption conflicts with a range of systematic biases, measurement errors and other inaccuracies that are well documented in the literature. To investigate the effects of such errors we introduce a framework for simulating systematic bias in attributed networks. Our framework enables us to model erroneous edge observations that are driven by external node attributes or errors arising from the (hidden) network structure itself. We exemplify how systematic inaccuracies distort conclusions drawn from network analyses on the task of minority representations in degree-based rankings. By analysing synthetic and real networks with varying homophily levels and group sizes, we find that the effect of introducing systematic edge errors depends on both the type of edge error and the level of homophily in the system: in heterophilic networks, minority representations in rankings are very sensitive to the type of systematic edge error. In contrast, in homophilic networks we find that minorities are at a disadvantage regardless of the type of error present. We thus conclude that the implications of systematic bias in edge data depend on an interplay between network topology and type of systematic error. This emphasises the need for an error model framework as developed here, which provides a first step towards studying the effects of systematic edge-uncertainty for various network analysis tasks.Leonie NeuhäuserFelix I. StammFlorian LemmerichMichael T. SchaubMarkus StrohmaierSpringerOpenarticleEdge uncertaintyRankingsAttributed networksBiasSocial networksApplied mathematics. Quantitative methodsT57-57.97ENApplied Network Science, Vol 6, Iss 1, Pp 1-22 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Edge uncertainty Rankings Attributed networks Bias Social networks Applied mathematics. Quantitative methods T57-57.97
spellingShingle	Edge uncertainty Rankings Attributed networks Bias Social networks Applied mathematics. Quantitative methods T57-57.97 Leonie Neuhäuser Felix I. Stamm Florian Lemmerich Michael T. Schaub Markus Strohmaier Simulating systematic bias in attributed social networks and its effect on rankings of minority nodes
description	Abstract Network analysis provides powerful tools to learn about a variety of social systems. However, most analyses implicitly assume that the considered relational data is error-free, and reliable and accurately reflects the system to be analysed. Especially if the network consists of multiple groups (e.g., genders, races), this assumption conflicts with a range of systematic biases, measurement errors and other inaccuracies that are well documented in the literature. To investigate the effects of such errors we introduce a framework for simulating systematic bias in attributed networks. Our framework enables us to model erroneous edge observations that are driven by external node attributes or errors arising from the (hidden) network structure itself. We exemplify how systematic inaccuracies distort conclusions drawn from network analyses on the task of minority representations in degree-based rankings. By analysing synthetic and real networks with varying homophily levels and group sizes, we find that the effect of introducing systematic edge errors depends on both the type of edge error and the level of homophily in the system: in heterophilic networks, minority representations in rankings are very sensitive to the type of systematic edge error. In contrast, in homophilic networks we find that minorities are at a disadvantage regardless of the type of error present. We thus conclude that the implications of systematic bias in edge data depend on an interplay between network topology and type of systematic error. This emphasises the need for an error model framework as developed here, which provides a first step towards studying the effects of systematic edge-uncertainty for various network analysis tasks.
format	article
author	Leonie Neuhäuser Felix I. Stamm Florian Lemmerich Michael T. Schaub Markus Strohmaier
author_facet	Leonie Neuhäuser Felix I. Stamm Florian Lemmerich Michael T. Schaub Markus Strohmaier
author_sort	Leonie Neuhäuser
title	Simulating systematic bias in attributed social networks and its effect on rankings of minority nodes
title_short	Simulating systematic bias in attributed social networks and its effect on rankings of minority nodes
title_full	Simulating systematic bias in attributed social networks and its effect on rankings of minority nodes
title_fullStr	Simulating systematic bias in attributed social networks and its effect on rankings of minority nodes
title_full_unstemmed	Simulating systematic bias in attributed social networks and its effect on rankings of minority nodes
title_sort	simulating systematic bias in attributed social networks and its effect on rankings of minority nodes
publisher	SpringerOpen
publishDate	2021
url	https://doaj.org/article/8031096ebc0b46e7a56d0f09181872d0
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Simulating systematic bias in attributed social networks and its effect on rankings of minority nodes

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