Group mixing drives inequality in face-to-face gatherings

Oliveira, Marcos; Karimi, Fariba; Zens, Maria; Schaible, Johann; Génois, Mathieu; Strohmaier, Markus

doi:10.1038/s42005-022-00896-1

Cited by 11 publications

(10 citation statements)

References 30 publications

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“…Fariba Karimi 3 is an Assistant Professor at the Vienna University of Technology (TU Wien) and a group leader at the the Complexity Science Hub Vienna (CSH). Her research mainly focuses on computational and network approaches to address societal challenges such as gender disparities in collaboration and citation networks [20,26], visibility of minorities in social and technical systems [14,23,39], algorithmic biases [9,17,38], and sampling hard-to-reach groups [13,51]. Her research also touches upon the emergence of culture in Wikipedia [22,47], spreading of information and norms [25], and perception biases [29] by using mathematical models, digital traces and online experiments.…”

Section: Tutorsmentioning

confidence: 99%

Inequality and inequity in network-based ranking and recommendation algorithms

Espín-Noboa

Wagner

Strohmaier

et al. 2022

Sci Rep

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Though algorithms promise many benefits including efficiency, objectivity and accuracy, they may also introduce or amplify biases. Here we study two well-known algorithms, namely PageRank and Who-to-Follow (WTF), and show to what extent their ranks produce inequality and inequity when applied to directed social networks. To this end, we propose a directed network model with preferential attachment and homophily (DPAH) and demonstrate the influence of network structure on the rank distributions of these algorithms. Our main findings suggest that (i) inequality is positively correlated with inequity, (ii) inequality is driven by the interplay between preferential attachment, homophily, node activity and edge density, and (iii) inequity is driven by the interplay between homophily and minority size. In particular, these two algorithms reduce, replicate and amplify the representation of minorities in top ranks when majorities are homophilic, neutral and heterophilic, respectively. Moreover, when this representation is reduced, minorities may improve their visibility in the rank by connecting strategically in the network. For instance, by increasing their out-degree or homophily when majorities are also homophilic. These findings shed light on the social and algorithmic mechanisms that hinder equality and equity in network-based ranking and recommendation algorithms.

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Section: Tutorsmentioning

confidence: 99%

Inequality and inequity in network-based ranking and recommendation algorithms

Espín-Noboa

Wagner

Strohmaier

et al. 2022

Sci Rep

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“…Understanding how groups interact in networks is fundamental for uncovering mechanisms underlying diverse phenomena, from protein interactions to social communication 1 – 3 . Such group-level interactions often generate mixing patterns in networks, commonly assessed with single-valued measures such as nominal assortativity 4 , 5 .…”

Section: Introductionmentioning

confidence: 99%

On the inadequacy of nominal assortativity for assessing homophily in networks

Karimi,

Oliveira

2023

Sci Rep

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Nominal assortativity (or discrete assortativity) is widely used to characterize group mixing patterns and homophily in networks, enabling researchers to analyze how groups interact with one another. Here we demonstrate that the measure presents severe shortcomings when applied to networks with unequal group sizes and asymmetric mixing. We characterize these shortcomings analytically and use synthetic and empirical networks to show that nominal assortativity fails to account for group imbalance and asymmetric group interactions, thereby producing an inaccurate characterization of mixing patterns. We propose the adjusted nominal assortativity and show that this adjustment recovers the expected assortativity in networks with various level of mixing. Furthermore, we propose an analytical method to assess asymmetric mixing by estimating the tendency of inter- and intra-group connectivities. Finally, we discuss how this approach enables uncovering hidden mixing patterns in real-world networks.

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“…However, merely increasing the group sizes of minorities without considering the social network structure and the position of social groups is insufficient to resolve structural inequalities. From the network-theoretic prospective, the visibility of minority groups in a network is dependent on both their relative sizes and the strength of group mixing biases 4,14 . Homophily, "similarity breeds connection" is one of those fundamental tie formation mechanisms that leads a higher tendency for ingroup mixing and often associated closely with attributes such as gender, race, and ethnic backgrounds 15 .…”

Section: Introductionmentioning

confidence: 99%

“…From the network-theoretic prospective there is a mathematical limit in which minorities can enhance their network visibility. When minorities are numerically small, the majority have the power and resources to make effective network changes 14 . In other words, behavioural change without institutional enforcement may not be enough to decrease inequality in visibility.…”

Section: Introductionmentioning

confidence: 99%

Improving the visibility of minorities through network growth interventions

Neuhäuser¹,

Karimi²,

Bachmann³

et al. 2022

Preprint

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Improving the position of minorities in networks via interventions is a challenge of high theoretical and societal importance. In this work, we examine how different network growth interventions impact the position of minority nodes in degree rankings over time. We distinguish between two kinds of interventions: (i) group size interventions, such as introducing quotas, that regulate the ratio of incoming minority and majority nodes; and (ii) behavioural interventions, such as homophily, i.e. varying how groups interact and connect to each other. We find that even extreme group size interventions do not have a strong effect on the position of minorities in rankings if certain behavioural changes do not manifest at the same time. For example, minority representation in rankings is not increased by high quotas if the actors in the network do not adopt homophilic behaviour. As a result, a key finding of our research is that in order for the visibility of minorities to improve, group size and behavioural interventions need to be coordinated. Moreover, their potential benefit is highly dependent on pre-intervention conditions in social networks. In a real-world case study, we explore the effectiveness of interventions to reach gender parity in academia. Our work lays a theoretical and computational foundation for further studies aiming to explore the effectiveness of interventions in growing networks.

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Group mixing drives inequality in face-to-face gatherings

Cited by 11 publications

References 30 publications

Inequality and inequity in network-based ranking and recommendation algorithms

Inequality and inequity in network-based ranking and recommendation algorithms

On the inadequacy of nominal assortativity for assessing homophily in networks

Improving the visibility of minorities through network growth interventions

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