Minorities in networks and algorithms

@article{Karimi2022MinoritiesIN,
  title={Minorities in networks and algorithms},
  author={Fariba Karimi and Marcos Oliveira and Markus Strohmaier},
  journal={ArXiv},
  year={2022},
  volume={abs/2206.07113}
}
In this chapter, we provide an overview of recent advances in data-driven and theoryinformed complex models of social networks and their potential in understanding societal inequalities and marginalization. We focus on inequalities arising from networks and network-based algorithms and how they affect minorities. In particular, we examine how homophily and mixing biases shape large and small social networks, influence perception of minorities, and affect collaboration patterns. We also discuss… 

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References

SHOWING 1-10 OF 74 REFERENCES
Link recommendations: Their impact on network structure and minorities
TLDR
This systematic experimentation helps to better understand when link recommendation algorithms are beneficial or harmful to minority groups in social networks, and suggests that, while all algorithms tend to close triangles and increase cohesion, all algorithms except Node2Vec are prone to favor and suggest nodes with high in-degree.
Homophily influences ranking of minorities in social networks
TLDR
A social network model is devised with tunable homophily and group sizes, and it is demonstrated how the degree ranking of nodes from the minority group in a network is a function of (i) relative group sizes and (ii) the presence or absence of homophilic behaviour.
Inequality and inequity in network-based ranking and recommendation algorithms
TLDR
This work proposes a directed network model with preferential attachment and homophily (DPAH) and demonstrates the influence of network structure on the rank distributions of PageRank and Who-to-Follow and sheds light on the social and algorithmic mechanisms that hinder equality and equity in network-based ranking and recommendation algorithms.
Network Effects and Social Inequality
Students of social inequality have noted the presence of mechanisms militating toward cumulative advantage and increasing inequality. Social scientists have established that individuals' choices are
Homophily and minority-group size explain perception biases in social networks
TLDR
It is shown that both over- and underestimation of the size of a minority group can emerge solely from structural properties of social networks, using a generative network model.
The "Majority Illusion" in Social Networks
TLDR
A statistical model is developed that quantifies the effect of the majority illusion and shows that the illusion is exacerbated in networks with a heterogeneous degree distribution and disassortative structure.
Sampling from Social Networks with Attributes
TLDR
It is concluded that uninformed sampling from social networks with attributes thus can significantly impair the ability of researchers to draw valid conclusions about the centrality of nodes and the visibility or invisibility of groups in social networks.
The Effect of Homophily on Disparate Visibility of Minorities in People Recommender Systems
TLDR
The findings suggest that the way and the extent to which people recommenders can produce disparate visibility on the two subgroups, might depend in large part on the level of homophily within the subgroups.
Structural inequalities exacerbate infection disparities: A computational approach
Background : During the COVID-19 pandemic, we witnessed a disproportionate infection rate among marginalized and low-income groups. Despite empirical evidence suggesting that structural inequalities
Modern temporal network theory: a colloquium
TLDR
This colloquium reviews the methods to analyze and model temporal networks and processes taking place on them, focusing mainly on the last three years, which includes the spreading of infectious disease, opinions, rumors, in social networks; information packets in computer networks; various types of signaling in biology, and more.
...
...