Presence Of Homophily Research Articles

Bridging the polarization gap: Maximizing diffusion among dissimilar communities

Polarized networks, composed of weakly connected and self-reinforcing groups, can limit the diffusion of ideas, behaviors, and innovations. Here, we use a complex contagion model, in which diffusion depends on both the connectivity and the similarity of individuals, to ask how to optimally build bridges and enhance diffusion in networks characterized by fragmentation and homophily. First, we show that the problem is NP-hard. Then, we explore the space of solutions using heuristics, finding that connecting high degree nodes, or hubs, is an ineffective strategy to accelerate diffusion in fragmented and homophilous networks. We show that in these networks, diffusion is more effectively accelerated by connecting similar but low degree nodes. These results tell us that, in the presence of homophily and polarization, connecting communities through their most central actors may impede rather than facilitate diffusion. Instead, strategies to accelerate the diffusion of innovation, behaviors, and ideas should focus on creating links among the most similar members of different communities. These findings shed light on the diffusion of ideas and innovations in polarized networks. CCS Concepts: • Mathematics of computing → Network optimization; • Information systems → Social networks

Effect of homophily and correlation of beliefs on COVID-19 and general infectious disease outbreaks.

Contact between people with similar opinions and characteristics occurs at a higher rate than among other people, a phenomenon known as homophily. The presence of clusters of unvaccinated people has been associated with increased incidence of infectious disease outbreaks despite high population-wide vaccination rates. The epidemiological consequences of homophily regarding other beliefs as well as correlations among beliefs or circumstances are poorly understood, however. Here, we use a simple compartmental disease model as well as a more complex COVID-19 model to study how homophily and correlation of beliefs and circumstances in a social interaction network affect the probability of disease outbreak and COVID-19-related mortality. We find that the current social context, characterized by the presence of homophily and correlations between who vaccinates, who engages in risk reduction, and individual risk status, corresponds to a situation with substantially worse disease burden than in the absence of heterogeneities. In the presence of an effective vaccine, the effects of homophily and correlation of beliefs and circumstances become stronger. Further, the optimal vaccination strategy depends on the degree of homophily regarding vaccination status as well as the relative level of risk mitigation high- and low-risk individuals practice. The developed methods are broadly applicable to any investigation in which node attributes in a graph might reasonably be expected to cluster or exhibit correlations.

(When) Does Appearance Matter? Evidence from a Randomized Controlled Trial

While there is evidence about labor market discrimination based on race, religion, and gender, we know little about whether physical appearance leads to discrimination in labor market outcomes. We deploy a randomized experiment on 1,000 respondents in India between April and May 2020 to investigate how ``looks'' affect labor market outcomes. Using machine learning and computer face rendering techniques, we simulate North, South, and North-eastern Indian faces and randomize two waves of treatments where we attach faces with otherwise identical CVs. However, the second wave also contains a vignette where we provide respondents with information about the incidence of discrimination. We focus on North-eastern Indian faces that resemble those in China to focus on the potential for discrimination that has historically been present in anecdotal accounts. While we do not find evidence of discrimination against North-eastern faces in the first wave, we do so in the second among those who do not see the vignette. We provide suggestive evidence that these results are consistent with the presence of homophily under limited attention.

Social capital and homophily both matter for labor market outcomes – evidence from replication and extension

Social resource theory has been challenged, as the effects of contact resources on job outcomes may be spurious given the presence of homophily. We review the Mouw–Lin debate and propose that occupational homophily moderates the role of contact resources in the labor market and that effects of resources depend on labor market institutions. We analyze data from the US, East Germany before 1989, and post-reform China, combining the first-difference method and Heckman selection procedure to deal with endogeneity. Empirical findings from different labor market contexts demonstrate that: (1) contact resources have a causal and positive role in job outcomes; (2) in a market economy, the role of resources is more salient if they provide within-occupation job-leads; (3) under state job-assignment systems occupational homophily does not pay off; and (4) job-search ties can take the form of heterophilous selection.

Adolescent Peer Choice and Cigarette Smoking: Evidence of Active Gene–Environment Correlation?

Both peer groups and genetics have been associated with adolescent smoking behavior. Recently, Loehlin (Loehlin, J. C. (2010). Is there an active gene-environment correlation in adolescent drinking behavior? Behavior Genetics, 40, 447-451) reported that twin differences in alcohol use were associated with differences in the number of common friends. Twins with more common friends were more similar in drinking, but only for dizygotic pairs. Using the same sample as Loehlin's (the National Merit twins), we replicated all of these findings for a composite cigarette smoking measure and for smoking initiation, but not persistence. The pattern of results is most consistent with homophily, or the tendency to associate with individuals that are like oneself. If peer influence occurs in the presence of homophily, then active genotype-environment correlation will be induced.

Engineering social contagions: Optimal network seeding in the presence of homophily

AbstractWe use data on a real, large-scale social network of 27 million individuals interacting daily, together with the day-by-day adoption of a new mobile service product, to inform, build, and analyze data-driven simulations of the effectiveness of seeding (network targeting) strategies under different social conditions. Three main results emerge from our simulations. First, failure to consider homophily creates significant overestimation of the effectiveness of seeding strategies, casting doubt on conclusions drawn by simulation studies that do not model homophily. Second, seeding is constrained by the small fraction of potential influencers that exist in the network. We find that seeding more than 0.2% of the population is wasteful because the gain from their adoption is lower than the gain from their natural adoption (without seeding). Third, seeding is more effective in the presence of greater social influence. Stronger peer influence creates a greater than additive effect when combined with seeding. Our findings call into question some conventional wisdom about these strategies and suggest that their overall effectiveness may be overestimated.

How Homophily Affects the Speed of Learning and Best-Response Dynamics

AbstractWe examine how the speed of learning and best-response processes depends on homophily: the tendency of agents to associate disproportionately with those having similar traits. When agents' beliefs or behaviors are developed by averaging what they see among their neighbors, then convergence to a consensus is slowed by the presence of homophily but is not influenced by network density (in contrast to other network processes that depend on shortest paths). In deriving these results, we propose a new, general measure of homophily based on the relative frequencies of interactions among different groups. An application to communication in a society before a vote shows how the time it takes for the vote to correctly aggregate information depends on the homophily and the initial information distribution.

How Homophily Affects the Speed of Learning and Best Response Dynamics

We examine how the speed of learning and best-response processes depends on homophily: the tendency of agents to associate disproportionately with those having similar traits. When agents’ beliefs or behaviors are developed by averaging what they see among their neighbors, then convergence to a consensus is slowed by the presence of homophily, but is not influenced by network density (in contrast to other network processes that depend on shortest paths). In deriving these results, we propose a new, general measure of homophily based on the relative frequencies of interactions among different groups. An application to communication in a society before a vote shows how the time it takes for the vote to correctly aggregate information depends on thehomophily and the initial information distribution.