Homophily influences ranking of minorities in social networks

Karimi, Fariba; Génois, Mathieu; Wagner, Claudia; Singer, Philipp; Strohmaier, Markus

doi:10.1038/s41598-018-29405-7

Cited by 133 publications

(179 citation statements)

References 37 publications

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“…To systematically study the relation between perception biases and network structure, we developed a network model that allows us to create scale-free networks with tunable homophily and minority-group sizes 28 . This network model is a variation of the Barabási-Albert preferential attachment model with the addition of a homophily parameter h (we call this model BA-homophily).…”

Section: Generative Network Model With Tunable Homophily and Minoritymentioning

confidence: 99%

Homophily and minority-group size explain perception biases in social networks

et al. 2019

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People's perceptions about the frequency of attributes in their social networks sometimes show false consensus, or overestimation of the frequency of own attributes, and sometimes false uniqueness, or underestimation of the frequency. Here we show that both perception biases can emerge solely from the structural properties of social networks. Using a generative network model, we show analytically that perception biases depend on the level of homophily and its asymmetric nature, as well as on the size of minority group. Model predictions correspond to empirical data from a cross-cultural survey study and to numerical calculations on six real-world networks. We also show in what circumstances individuals can reduce their biases by relying on perceptions of their neighbors. This study advances our understanding of the impact of network structure on perception biases and offers a quantitative approach for addressing related issues in society. arXiv:1710.08601v4 [physics.soc-ph] 22 Jul 2019 Empirical networksThe first network, Brazilian network, captures sexual contact between sex workers and sex buyers 46 . The network consists of 16, 730 nodes and 39, 044 edges. There are 10, 106 sex workers and 6, 624 sex buyers (minority-group size f a = 0.4). In this network, no edges among members of the same group exist resulting in the Newman's assortativity (q = −1), and consequently, the network is purely heterophilic (h = 0).The second network is an online Swedish dating network from PussOKram.com (POK) 47 . This network contains 29, 341 nodes with strong heterophily (h = 0.17, q = −0.65). Given the high bipartivity of the network, we are able to infer the group of nodes using the max-cut greedy algorithm. The results are in good agreement with the bipartivity reported 48 . Since the group definition is arbitrary, we label the nodes based on their relative group size as minority gender and majority gender. Here, the fraction of the minority in the network is 0.44.The third network is a Facebook network of a university in the United States (USF51) 49 . The network is composed of 6,253 nodes and includes information about individuals' gender. In this network male students are in the minority, occupying 42% of the network, and the network exhibits a small heterophily 49 (q = −0.06, h = 0.48).The fourth network is extracted from the collaborative programming environment GitHub. The network is a snapshot of the community (extracted August 4, 2015) that includes information about the first name and family name of the programmers. We used the first name and family name to infer the gender of the programmers 50 . After we removed ambiguous names, the network consisted of 120, 338 men and 7, 330 women. Here, women belong to the minority group and represent only about 6% of the population. The network displays a moderately symmetric gender homophily of 0.53 (q = 0.07).The fifth network depicts scientific collaborations in computer science and is extracted from Digital Bibliography & Library Project's website (DBLP) 51 . We used a new...

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Section: Generative Network Model With Tunable Homophily and Minoritymentioning

confidence: 99%

Homophily and minority-group size explain perception biases in social networks

et al. 2019

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“…Connecting these m ties from the new agent to existing agents is probabilistic, and relies on the homophily parameter h and the degree of the present agents [64]. The parameter h ranges from 0 to 1 and defines the likelihood of agents to form ties to agents from the same group or from a different group (1 − h).…”

Section: Generation Of Network Structurementioning

confidence: 99%

“…In addition, the degree growth function has the following relation to the probability of linkage [64]:…”

Section: Appendix: Analytical Derivations For Norm Endorsementmentioning

confidence: 99%

The Role of Network Structure and Initial Group Norm Distributions in Norm Conflict

Kohne

Gallagher

Kirgil

et al. 2019

Computational Social Sciences

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Social norms can facilitate societal coexistence in groups by providing an implicitly shared set of expectations and behavioral guidelines. However, different social groups can hold different norms, and lacking an overarching normative consensus can lead to conflict within and between groups. In this chapter, we present an agent-based model that simulates the adoption of norms in two interacting groups. We explore this phenomenon while varying relative group sizes and homophily/heterophily (two features of network structure), and initial group norm distributions. Agents update their norm according to an adapted version of Granovetter's threshold model, using a uniform distribution of thresholds. We study the impact of network structure and initial norm distributions on the process of achieving norma-(eds). Computational Conflict Research. Cham: Springer Nature.) 1 2 J. Kohne, N. Gallagher, Z.M. Kirgil, R. Paolillo, L. Padmos, F. Karimi tive consensus and the resulting potential for intragroup and intergroup conflict. Our results show that norm change is most likely when norms are strongly tied to group membership. Groups end up with the most similar norm distributions when networks are heterophilic, with small to middling minority groups. High homophilic networks show high potential intergroup conflict and low potential intragroup conflict, while the opposite pattern emerges for high heterophilic networks.

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“…For the purpose of modeling normative conflict in social networks with respect to relative group sizes, homophily/heterophily, and group norm differences, we developed a modular simulation framework based on a network generation algorithm using preferential attachment, group size and homophily/heterophily (Karimi et al, 2018), and Granovetter's threshold model . We utilized R (R Core Team, 2019) for our model as it appears to be more widespread among the social science community than Python and offers more customizability, better parallelization, and scalability than NetLogo.…”

Section: Agent-based Modelmentioning

confidence: 99%

“…In our agent-based model, we aim to simulate the impact of group size, homophily/heterophily between agents from different groups, and initial group norm distributions on the process of reaching normative consensus and resulting conflict potential. To this end, we generated networks with 2000 agents each, where network structure is determined by one parameter for relative group size (g) and one parameter for homophilic/heterophilic preferences of agents (h) (Karimi et al, 2018). In addition, initial norms for agents were assigned based on three different pairs of binomial probabilities, resulting in three conditions for initial group norm distributions.…”

Section: Simulating Norm Conflictmentioning

confidence: 99%

Computational Conflict Research

Deutschmann¹,

Lorenz²,

Nardin³

et al. 2020

Computational Social Sciences

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Homophily influences ranking of minorities in social networks

Cited by 133 publications

References 37 publications

Homophily and minority-group size explain perception biases in social networks

Homophily and minority-group size explain perception biases in social networks

The Role of Network Structure and Initial Group Norm Distributions in Norm Conflict

Computational Conflict Research

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